Genetic algorithms operate on a set of possible solutions. Because of the random nature of genetic algorithms, solutions found by an algorithm can be good, poor, or infeasible [defective, erroneous], so there should be a way to specify how good that solution is. This is done by assigning a fitness value [or just fitness] to the solution. Chromosomes represent solutions within the genetic algorithm. The two basic components of chromosomes are the coded solution and its fitness value.
Chromosomes are grouped into population [set of solutions] on which the genetic algorithm operates. In each step [generation], the genetic algorithm selects chromosomes from a population [selection is usually based on the fitness value of the chromosome] and combines them to produce new chromosomes [offsprings]. These offspring chromosomes form a new population [or replace some of the chromosomes in the existing population] in the hope that the new population will be better than the previous ones. Populations keep track of the worst and the best chromosomes, and stores additional statistical information which can be used by the genetic algorithm to determine the stop criteria.
A chromosome, in some way, stores the solution which it represents. This is called the representation [encoding] of the solution. There are a number of probable ways to represent a solution in such a way that it is suitable for the genetic algorithm [binary, real number, vector of real number, permutations, and so on] and they mostly depend on the nature of the problem.
Diagram - Chromosome representations [for maximization of a single-parameter function]
Diagram - Chromosome representations [Traveling Salesman Problem]
Genetic algorithms produce new chromosomes [solutions] by combining existing chromosomes. This operation is called crossover. A crossover operation takes parts of solution encodings from two existing chromosomes [parents] and combines them into a single solution [new chromosome]. This operation depends on the chromosome representation, and can be very complicated. Although general crossover operations are easy to implement, building specialized crossover operations for specific problems can greatly improve the performance of the genetic algorithm.
Diagram - Crossover operation examples
Before a genetic algorithm finishes the production of a new chromosome, after it performs a crossover operation, it performs a mutation operation. A mutation operation makes random, but small, changes to an encoded solution. This prevents the falling of all solutions into a local optimum and extends the search space of the algorithm. Mutations as well as crossover operations depend on the chosen representation.
Diagram - Mutation operation examples [swap mutation is performed over the first, and over the second, an invert mutation is performed]
Crossover and mutation operations are not always performed when producing a new chromosome. If crossover is not performed, the genetic algorithm produces a new chromosome by copying one of the parents. The rates of crossover and mutation operations are called crossover probability and mutation probability, respectively. The crossover probability is usually high [about 80%], and the mutation probability should be relatively low [about 3%, but for some problems, a higher probability gives better results]. A higher mutation probability can turn the genetic algorithm in to a random search algorithm.
The last operations defined by genetic algorithms used to manipulate chromosomes are fitness operations and fitness comparators. A fitness operation measures the quality of the produced solution [chromosome]. This operation is specific to the problem, and it actually tells the genetic algorithm what to optimize. Fitness comparators [as their name suggests] are used to compare chromosomes based on their fitness. Basically, a fitness comparator tells the genetic algorithm whether it should minimize or maximize the fitness values of chromosomes.
Choosing parents for the production of new chromosomes from a population is called selection. Selection can be based on many different criteria, but it is usually based on the fitness value. The idea behind this is to select the best chromosomes from the parents in the hope that combining them will produce better offspring chromosomes. But, selecting only the best chromosomes has one major disadvantage, all chromosomes in a population will start to look the same very quickly. This narrows the exploration space, pushes the genetic algorithm into the local optimum, and prevents the genetic algorithm from finding possibly better solutions that reside in inaccessible areas of the exploration space. To preserve the diversity of chromosomes [and a wider exploration space] within the population, selection operations usually introduce a factor of randomness in the selection process. Some implementations of selection operations are entirely random.
One problem may occur with selection operations that are based on fitness values. When there is a chromosome with a dominant fitness value, it will be selected most of the times, thus it will cause problems similar to the existing ones. To prevent this, fitness values can be scaled [transformed] to lower the difference between dominant chromosome(s) and the rest of the population [this allows other chromosomes to be selected]. There are many ways to transform a fitness value. Usually, they are implemented by applying a mathematical transformation to the fitness value, but there are other methods like ranking based scaling that use the rank [based on the raw fitness values of chromosomes] of a chromosome as the scaled fitness value.
Diagram - Scaling fitness value [shows the selection probability of chromosomes]
A coupling operation defines how the selected chromosomes [parents] are paired for mating [mating is done by performing a crossover operation over the paired parents and applying a mutation operation to the newly produced chromosome]. This operation gives better control over the production of new chromosomes, but it can be skipped and new chromosomes can be produced as the selection operation selects parents from the population.
Diagram - Coupling operation flowchart
Diagram - Selection operation without coupling operation flowchart
The next step performed by a genetic algorithm is the introduction of new chromosomes into a population. Offspring chromosomes can form a new population and replace the entire [previous] population [non-overlapping population], or they can replace only a few chromosomes in the current population [overlapping population]. For overlapping populations, the replacement operation defines which chromosomes are removed [usually the worst chromosomes] from the current population and which offspring chromosomes are inserted. By replacing chromosomes, there is a chance that the genetic algorithm will lose the best chromosome[s] [found so far]. To prevent this, the concept of elitism is introduced into genetic algorithms. Elitism guarantees that the best chromosome[s] from the current generation are going to survive to the next generation.
An algorithm performs the previously described steps one by one in sequence, and when they have been performed, it is said that a generation has passed. At the end of each generation, the genetic algorithm checks the stop criteria. Because of the nature of genetic algorithms, most of the time, it is not clear when the algorithm should stop, so a criteria is usually based on statistical information such as the number of the generation, the fitness value of the best chromosome, or the average fitness value of the chromosomes in the population, the duration of the evolution process...
Diagram - Flowchart of a genetic algorithm [overlapping population coupling operation]
Diagram - Flowchart of a genetic algorithm [overlapping population without coupling operation]
Diagram - Flowchart of a genetic algorithm [non-overlapping population with coupling operation]
This is a brief introduction to the design and the structure of the Genetic Algorithm Library. The library is a set of C++ classes that represent the building blocks of genetic algorithms.
Note: For more details about changes in recent versions of the library see this section of the article.
The following diagram illustrate the basic structure of the Genetic Algorithm Library:
Diagram - Structure of the Genetic Algorithm Library
The first layer mainly contains classes that are not directly related to genetic algorithms but are essential for their implementation. The Genetic Algorithm Library implements random number generators, a set of classes for platform-independent threading and synchronization, smart pointers for easier management of memory [primarily for automatic management of memory used by chromosomes], and catalogues [catalogues are used to store and keep track of currently available genetic operations].
Except these general-purpose features, the library provides some more GA specific stuff at the lowest layer, such as classes for keeping track of statistical information of genetic algorithms and observing the framework. Interfaces for genetic operations and parameters' objects are also defined at this layer.
Together, these features provide common functionality that is used by other, higher layers of the library. Classes of this layer are split in several namespaces.
The mid-layer part is split into three namespaces, as shown in the diagram. The majority of the core features of the library are implemented at this layer.
First of all, the Chromosome
namespace contains a set of interfaces and classes used to represent chromosomes in the library and to define their basic behavior in the system. This namespace contains the declaration of interfaces for four types of genetic operations: crossover, mutation, fitness operation, and fitness comparator.
The second namespace is the Population
namespace. The central class of this namespace is a class that manages the population of chromosomes, stores statistical information, and tracks the best and the worst chromosomes. Interfaces for selection, coupling, replacement, and scaling operations are defined in this namespace.
The last namespace, Algorithm
, defines interfaces for genetic algorithms, and implements some of their basic behaviors. This namespace also defines an interface for operations that implement the stop criteria of the algorithms.
These two layers represent the core of the library.
The top layer of the library implements the earlier-mentioned genetic operations, chromosome representations, and genetic algorithms. As mentioned, all built-in genetic operations are sorted in appropriate catalogues.
Diagram - Namespaces
Chromosomes are the central objects in a genetic algorithm. Chromosomes are defined by the GaChromosome
class in this library.
Diagram - GaChromosome
class
GaChromosome
is the interface for the actual implementations of chromosomes. This class [interface] defines the methods Crossover
, Mutation
, CalculateFitness
, and CompareFitness
which represent the previously described genetic operations [mutation, crossover, fitness operation, and fitness comparator].
The MakeCopy
method represents a virtual copy constructor. New classes should override this method, and it should return a new instance of the chromosome's object. This method can copy an entire chromosome [setup and coded solution], or it can copy just the chromosome's setup [leaving empty encoding]. The MakeFromPrototype
method makes a new chromosome object with the same setup as the current object, but it initializes the encoding of the solutions randomly.
Each chromosome has defined parameters [such as crossover and mutation probability]; these parameters are represented by the GaChromosomeParams
class.
Diagram - GaChromosomeParams
class
GaChromosomeParams
defines the mutation and crossover probability, the size of the mutation, and the number of crossover points, as well as the "improving-only mutation" flag. The default chromosome parameters initialized by the default constructor are:
- mutation probability: 3%
- mutation size: 1 [only one value of coded solution is mutated]
- only improving mutations are accepted: yes
- crossover probability: 80%
- number of crossover points: 1
All parameter classes in the library inherit the GaParameters
class.
Diagram - GaParameters
class
This class [interface] defines the Clone
method which represents a virtual copy constructor, and it should return a pointer to the new parameters object which is the copy of the current object.
The GaChromosomes
class is an interface, and it does not implement any behaviors. Still, some basic features are common to all chromosome types [storing chromosome parameters and fitness value]; these features are implemented by the GaDefaulChromosome
class. Besides parameters, chromosomes can have other configuration data [Chromosome Configuration Block or CCB], and these data are usually same for all chromosomes in the population. Storing a chromosome's configuration data is defined by the GaChromosomeParamBlock
class.
Diagram - GaDefaultChromosome
and GaChromosomeParamsBlock
classes
The Crossover
and Mutation
methods of the GaDefaultChromosome
class performs these genetic operations only with probability defined by the chromosome's parameters. If the operations should be performed, these methods call PerformCrossover
and PerformMutation
. New chromosomes that inherit the GaDefaultChromosome
class should override PerformCrossover
and PerformMutation
, not the Crossover
and Mutation
methods.
This class also introduces a framework for improving-only mutations. Before the mutation operation is executed, the PrepareForMutation
method is called. This method should backup the old chromosome, and then the mutation is performed. After that, the old fitness of the chromosome [before mutation] and the new one are compared. If the mutation has improved fitness, it is accepted, and the AcceptMutation
method is called; otherwise, the RejectMutation
method is called. If the "improving-only mutation" flag is not set, mutations are immediately accepted without calls to these methods.
Crossover, mutation, and fitness operations can be implemented by inheriting the already defined class that implements specific types of chromosomes. Then, the user can override the PerformCrossover
[or Crossover
], PerformMutation
[or Mutation
], and CalculateFitness
methods and implement specific operations for the targeted problem.
The Genetic Algorithm Library provides another way to accomplish this. These genetic operations can be defined and implemented in separated classes. Then, references to objects of these classes [called functors] can be stored in the CCB. This allows the user to change genetic operations at runtime [which is not possible with the previously described method].
Diagram - GaDynamicOperationChromosome
class and interfaces for genetic operations
GaDynamicOperationChromosome
overrides the PerformCrossover
, PerformMutation
, CalculateFitness
, and CompareFitnesses
methods, and routes calls to functors of genetic operations stored in the CCB.
The CCB, represented by the GaChromosomeOperationsBlock
class, stores these functors.
GaCrossoverOperation
is the interface for the crossover operation. User defined classes should override operator()
:
virtual GaChromosomePtr operator ()(
const GaChromosome* parent1,
const GaChromosome* parent2) const;
The parameters are pointers to the parents that are used by the crossover operation. The operator should return a smart pointer to the produced offspring chromosome.
GaMutationOperation
is the interface for the mutation operation. The user defined classes should override operator()
:
virtual void operator ()(GaChromosome* chromosome) const;
This operator takes [as parameter] a pointer to the chromosome on which this operation should be performed.
GaFitnessOperation
is the interface for the fitness operation. The user defined classes should override operator()
:
virtual float operator ()(const GaChromosome* chromosome) const;
This operator takes [as parameter] a pointer to the chromosome on which this operation should be performed, and returns the calculated fitness value of the chromosome.
The last operation is the fitness comparator. The interface for fitness comparators are defined by the GaFitnessComparator
class. The user defined classes should override operator()
:
virtual int operator ()
float fitness1,
float fitness2) const;
This operator takes two fitness values as parameters, and returns an integer:
-1
if the first fitness value is lower than the second 0
if these two values are equal 1
if the first value is higher than the second
All classes that implement genetic operations in the library inherit the GaOperation
class.
Diagram - GaOperation
class
MakeParameters
makes the parameters object that is needed by the operation, and returns a pointer to the object. If the operation does not require parameters, the method can return a NULL
pointer. The CheckParameters
method checks the validity of the provided parameters, and returns true
it they are valid, or false
if they are invalid. All genetic operations must implement these two methods.
The Genetic Algorithm Library is designed to use stateless objects of genetic operations [functors]. Following that design, all built-in operations are stateless, but the library can handle user defined operations whose objects are not stateless.
Population objects of genetic algorithms are represented in this library by the GaPopulation
class.
Diagram - GaPopulation
class
A population object stores chromosomes and statistical information. Chromosomes in the population are represented by objects of the GaScaledChromosome
class. Objects of this class bind chromosomes to the population. Chromosomes, generally, store data which do not depend on the population in which the chromosomes reside, but there is a portion of information about chromosomes which are dependant [such as the scaled fitness, or the index of the chromosomes in the population]. These data are members of the GaScaledChromosome
class. It makes sharing of chromosomes among populations easier and more [memory] efficient.
Chromosomes can be stored in a population in sorted or unsorted order [by fitness value - scaled, or raw]. Whether the population should be sorted or not depends on other parts of the genetic algorithm [the selection operation, for instance], and it is controlled by the parameters of the population. It is also easier to track the best and the worst chromosomes when the population is sorted, but it is more time consuming. If it is not sorted, the population uses sorted groups [the GaSortedGroup
class] to track these chromosomes. Sorted groups store the indices of the chromosomes in the population. The number of tracked chromosomes [in both groups, the best and the worst] is defined by the parameters of the population. It is important to notice that tracking large numbers of chromosomes is inefficient; in such cases, it is probably better to use a sorted population.
When the population is created, it does not contain any chromosomes [it is not initialized]. The Initialize
method fills a population by making new chromosomes using the MakeFromPrototype
method of the provided prototype chromosome.
Diagram - Chromosomes in the population
The GaStatistics
class is used for storing and tracking the statistics of the population. Objects of this class stores information of the current and the previous generation of the population.
Diagram - GaStatistics
and support classes
The GaStatValue
template class stores a single statistical value. The GaStatValueType
enumeration defines the tracked statistical data. These data can be used to measure the progress of the algorithm, and they are usually employed by the stop criteria.
The behavior of the population is controlled by the parameters of the population. The GaPopulationParameters
class manages a population's parameters.
Parameters are only one segment of a population's configuration. Configuration also includes genetic operations [that are performed over the population - selection, coupling, replacement, and scaling] and their parameters. The GaPopulationConfiguration
class stores and manages configuration. Configuration can be shared among populations. The BindPopulation
method applies configuration and binds a population to it. UnbindPopulation
instructs a population that it should not use the configuration any more, and unbinds the population. When some aspect of the configuration is changed, all bound populations are notified.
When an object of a population's configuration is made and initialized using the default constructor, the constructor copies the default configuration. A reference to the default configuration can be obtained using the GetDefault
method. A user can change the default configuration at run-time. At start, the default configuration is initialized to:
- population parameters:
- population size: 10
- resizable: no
- sorted: yes
- scaled fitness value used for sorting: no
- track the best chromosomes: 0 [sorted population]
- track the worst chromosomes: 0 [sorted population]
- fitness comparator:
GaMaxFitnessComparator
- selection:
GaSelectRouletteWheel
, size: 2 - coupling:
GaInverseCoupling
, size: 2 - replacement:
GaReplaceWorst
, size: 2 - scaling: none
Diagram - Management of a population's configuration
Genetic operations and their parameters are stored as a pair in the configuration. The configuration uses the GaOperationParametersPair
class to store these pairs. A pair object stores a pointer to the operation object and a copy of the provided object of the operation's parameters.
Diagram - GaOperationParametersPair
class
An interface for selection operations are defined by the GaSelectionOperation
class. User defined classes should override operator()
:
virtual void operator ()(
const GaPopulation& population,
const GaSelectionParams& parameters,
GaSelectionResultSet& result) const;
A selection operation takes a reference to the population's object and a reference to the selection parameters. It also takes a reference to the result set which is used to store the selected chromosomes. A result set stores the indices [in the population] of the selected chromosomes in sorted order [by fitness value]. The GaSelectionResultSet
class wraps the GaSortdeGroup
class. The GaSelectionOperation
class has a method MakeResultSet
which makes a new instance of the result set and returns a reference to it. User defined classes can override this method if the selection operation requires a different type of result set.
The GaSelectionParams
is the base class for the parameters of selection operations. This class defines only one parameter [which is common for all selection operations], the selection size.
Diagram - Selection operation interface
An interface for coupling operations is defined by the GaCouplingOperation
class. User defined classes should override operator ()
:
virtual void GACALL operator ()(
const GaPopulation& population,
GaCouplingResultSet& output,
const GaCouplingParams& parameters,
int workerId,
int numberOfWorkers) const=0;
A coupling operation takes a reference to the population's object and a reference to the coupling parameters. It also takes a reference to the result set [the GaCouplingResultSet
class] which is used to store the produced offspring chromosomes and information about their parents.
A coupling operation can be executed concurrently by multiple working threads. The framework supplies a number of threads that execute the operation and an ID of the thread that executes the current call to the operation.
The GaCouplingParams
is the base class for the parameters of coupling operations. This class defines only one parameter [which is common for all coupling operations], the number of offspring chromosomes which should be produced.
Diagram - Coupling operation interface
An interface for replacement operations is defined by the GaReplacementOperation
class. User defined classes should override operator()
:
virtual void GACALL operator ()(
GaPopulation& population,
const GaReplacementParams& parameters,
const GaCouplingResultSet& newChromosomes) const;
A replacement operation takes a reference to the population's object, a reference to the replacement parameters, and a reference to the result set of the coupling operation which contains the offspring chromosomes that should be inserted in the population.
GaReplacementParams
is the base class for the parameters of replacement operations. This class defines only one parameter [which is common for all replacement operations], the number of chromosomes which should be replaced.
Diagram - Replacement operation interface
An interface for scaling operations is defined by the GaScalingOperation
class. User defined classes should override the operator()
, IsRankingBase
, and NeedRescaling
methods:
virtual float operator ()(
const GaScaledChromosome& chromosome,
const GaPopulation& population,
const GaScalingParams& parameters) const;
virtual bool IsRankingBased() const;
virtual bool NeedRescaling(
const GaPopulation& population,
const GaScalingParams& parameters) const;
operator()
takes a reference to the population's object and a reference to the scaling parameters. IsRankingBased
should return true
if the implementation of the scaling operation is based on the ranking of chromosomes. Otherwise, it should return false
. GaScalingParams
is the base class for the parameters of the scaling operations.
Scaling can be based on values that can change from generation to generation, or it can use values that remain constant during a longer time of evaluation process, or values that are not changed at all. Scaled fitness values are calculated when a new chromosome is inserted in to the population, but the changing of the population may require rescaling of the fitness values of all the chromosomes in the population. The NeedRescaling
method is called by the framework to check whether rescaling is required at the end of each generation. If this method returns true
, the framework rescales the fitness values of all the chromosomes in the population.
Diagram - Scaling operation interface
A genetic algorithm object is a glue for the described building blocks. It defines and controls the evolution process, and determines its end. An interface for genetic algorithms is defined by the GaAlgorithm
class.
Diagram - GaAlgorithm
class
The StartSolving
, StopSolving
, and PauseSolving
methods allow users to control the evolution process, and the GetState
method can be used to obtain its current state. When the user changes any part of the algorithm [the genetic operation or its parameters] during runtime, the BeginParameterChange
method should be called before any change takes place. When the user finishes changes, the user must call the EndParameterChange
method.
The GaAlgorithmParams
class represents the base class for the algorithm's parameters.
A genetic algorithm notifies the rest of the system about changes in the evolution process through the Observer pattern. The user must call the SubscribeObserver
method to start receiving these notifications. When notifications are no longer required, the user can call the UnsubscribeObserver
method. Objects that are passed to these two methods must be instances of classes inherited from the GaObserver
[or GaObserverAdapter
] class.
Diagram - Algorithm observing
GaObserver
is the interface for the observers of the genetic algorithm. Implementations of methods of this interface are actually event handlers. When an event occurs in the genetic algorithm, the algorithm walks through the list of subscribed observers and calls the appropriate observers that handle the event.
virtual void StatisticUpdate(
const GaStatistics& statistics,
const GaAlgorithm& algorithm);
| Notifies the observer when the statistics of the algorithm has been changed. This event occurs at the end of each generation. |
void NewBestChromosome(
const GaChromosome& newChromosome,
const GaAlgorithm& algorithm);
| This event occurs when the algorithm finds new chromosomes that are better than the best chromosomes previously found. |
void EvolutionStateChanged(
GaAlgorithmState newState,
const GaAlgorithm& algorithm);
| The event notifies the observer when the user starts, stops, or pauses the evolution process or when the algorithm reaches the stop criteria. |
Lists of observers are managed by GaObserverList
. This class also implements the GaObserver
interface, but instead of handling events, it routes notifications to all the subscribed observers. operator+=
and operator-=
are used to subscribe and unsubscribe observers.
observerList += observer;
observerList -= observer;
When a user-defined observer inherits the GaObserver
class, it must implement all the defined methods. Sometimes, a user does not want to receive all the events. In this case, the user can use the GaObserverAdapter
class as the base class for the observer. The GaObserverAdapter
class implements all the methods defined by the GaObserver
, with default event handling; the user can override only those methods that handle the desired events.
The end of the evolution process is determined by the stop criteria. The Genetic Algorithm Library defines the GaStopCriteria
class that represents an interface for this genetic operation. The user defined class that implements the stop criteria should override the Evaluate
method:
virtual bool Evaluate(
const GaAlgorithm& algorithm,
const GaStopCriteriaParams& parameters) const;
This method takes a reference to the algorithm's object whose state is evaluated and a reference to the parameters of the stop criteria. If the algorithm has reached the required state and should stop evolution, this method should return true
. If the criteria is not reached, the method should return false
.
GaStopCriteriaParams
is the base class for the parameters of the stop criteria.
Some default behaviors of the genetic algorithm are implemented in the GaBaseAlgorithm
class.
Diagram - GaBaseAlgorithm
class
This class manages the state and state transitions of the evolution process. The following diagram illustrates the possible states of the evolution process, transitions, actions that trigger transitions, and reactions to the changes of the state.
Diagram - Algorithm's states
GaBaseAlgorithm
implements the StartSolving
, StopSolving
, and PauseSolving
methods that control the evolution process. These methods perform state checks and state transitions. When the state of the evolution process is changed, one of the following virtual methods is called: OnStart
, OnResume
, OnPause
, OnStopt
. New classes that implement specific genetic algorithms should override these methods to handle state changes.
This class also manages a list of subscribed observers, and handles runtime changes by implementing BeginParameterChange
and EndParameterChange
methods that protect concurrent changes from multiple threads.
Genetic algorithms are convenient for parallelization because they operate on set of independent solutions. This allows genetic algorithms to take advantage of modern multiprocessor architectures with low [implementation and runtime] overheads.
The Genetic Algorithm Library provides a framework for parallel execution of genetic algorithms. This framework is built around the GaMultithreadingAlgorithm
class.
Diagram - GaMultithreadingAlgorithm
class
Each multithreaded algorithm has one control thread and at least one working thread [worker]. The control thread prepares work, and then it transfers control to the workers. After all workers finish their portion of work, the control thread gains control again and merges the results produced by the workers. This class manages all the used threads, so the user does not have to worry about the resources involved in multithreading.
The following diagram shows the control flow of a multithreaded algorithm:
Diagram - Multithreaded algorithm workflow
The GaMultithreadingAlgorithm
class overrides the OnStart
, OnResume
, OnPause
, and OnStop
methods to control the working and control the threads.
The ControlFlow
and WorkFlow
methods represent the flow of the control and the working threads. These methods provide synchronization and communication between the control thread, the workers, and the rest of the system. Before the ControlFlow
transfers control to the workers, it calls the BeforeWorkers
method, and after the workers finish, it calls the AfterWorkers
method. Genetic algorithm implementations can override these methods to do specific work preparations and work merging. When the worker gains control, the WorkFlow
method calls the WorkStep
method. This method should be used to perform all of the work that can be executed simultaneously.
The GaMultithreadingAlgorithmParams
is the base class for the parameters of multithreaded algorithms. It defines the number of workers involved in the execution of a genetic algorithm.
The Genetic Algorithm Library contains a set of classes, data types, and macros that isolate platform-dependent threading. The GaThread
class wraps and controls system threads.
Diagram - GaThread
class
The GaThreadStatus
enumeration defines the possible states of the thread and the GaThreadParameter
structure is used to store the start parameters of the thread.
The threading data types defined by the library:
SystemThread | This type defines the system specific type for storing thread objects. |
ThreadID | Variables/objects of this type are used for storing IDs of system threads. This type hides system specific types which are used for the purpose. |
ThreadFunctionPointer | This type defines a pointer to a function that is used as a thread's entry point. |
ThreadFunctionReturn | This type is used as a return value type for a function which is used as a thread's entry point. |
GaCriticalSection
isolates system-specific protection of critical sections from concurrent access by multiple threads. The GaSectionLock
class is used for automatic locking and unlocking of critical section objects.
GaCriticalSection _criticalSection;
void SomeMethod()
{
GaSectionLock lock( &_criticalSection, true );
}
Diagram - GaCriticalSection
and GaSectionLock
classes
The Genetic Algorithm Library defines macros that make synchronization with critical section objects easier. These macros are described later.
Synchronization data types:
SysSyncObject | This type defines system-specific synchronization objects that is used by the GaCriticalSection class. |
SysSemaphoreObject | This type defines system-specific semaphore objects. |
SysEventObject | This type defines system-specific event objects. |
Synchronization functions:
bool MakeSemaphore(
SysSemaphoreObject& semaphore,
int maxCount,
int initialCount);
| This function is used to create an operating system object for a semaphore and to initialize it. |
bool DeleteSemaphore(
SysSemaphoreObject& semaphore);
| This function is used to free the operating system semaphore object. |
bool LockSemaphore(
SysSemaphoreObject& semaphore);
| This function is used to acquire access to a critical section protected by a semaphore. |
bool UnlockSemaphore(
SysSemaphoreObject& semaphore,
int count);
| This function is used to release access to a critical section protected by a semaphore. |
bool MakeEvent(
SysEventObject& event,
bool intialState);
| This function is used to create an operating system object for an event and to initialize it. |
bool DeleteEvent(
SysEventObject& event);
| This function is used to free an operating system event object. |
bool WaitForEvent(
SysEventObject& event);
| This function is used to block a calling thread until an event reaches the signaled state. When the calling thread is released, an event is restarted to the non-signaled state. |
bool SignalEvent(
SysEventObject& event);
| This function is used to set an event to the signaled state. |
Synchronization macros:
ATOMIC_INC(VALUE)
| Atomically increments VALUE by one, and returns the new value. |
ATOMIC_DEC(VALUE)
| Atomically decrements VALUE by one, and returns the new value. |
SPINLOCK(LOCK)
| Protects a critical section with spinlock. LOCK must be a variable of int type. To release a spinlock, the LOCK variable should be set to 0. |
DEFINE_SYNC_CLASS
| This macro inserts members to a class which are needed to synchronize access to an instance of the class. The LOCK_OBJECT and LOCK_THIS_OBJECT macros are used to synchronize access to an object. |
LOCK(LOCK_NAME)
| This macro is used to acquire access to a critical section protected by the synchronization object (GaSectionLock or GaCriticalSection ). |
UNLOCK(LOCK_NAME)
| This macro is used when a thread exits a critical section and releases access to the synchronization object (GaSectionLock or GaCriticalSection ). |
LOCK_OBJECT(LOCK_NAME,OBJECT)
| This macro acquires access to an object with a built-in synchronizer and prevents concurrent access. It instantiates a GaSectionLock object with the name LOCK_NAME , and acquires access to the object. When execution leaves the scope in which LOCK_OBJECT is specified, the GaSectionLock object is destroyed and access to the locked object is released. Unlocking access to the object before leaving the scope can be done by calling the UNLOCK(LOCK_NAME) macro. |
LOCK_THIS_OBJECT(LOCK_NAME)
| This macro acquires access to this and prevents concurrent access. It declares and instantiates a GaSectionLock object with the name LOCK_NAME and acquires access to this object. When execution leaves the scope in which LOCK_OBJECT is specified, the GaSectionLock object is destroyed and access to this object is released. Unlocking access to the object before leaving the scope can be done by calling the UNLOCK(LOCK_NAME) macro. |
To use the LOCK_OBJECT
and LOCK_THIS_OBJECT
macros to synchronize access to an object, the class of the object must use the DEFINE_SYNC_CLASS
macro that injects members used by these macros.
Injected members are:
mutable GaCriticalSection _synchronizator
attribute and GaCriticalSection* GACALL GetSynchronizator() const
method
class SomeClass
{
DEFINE_SYNC_CLASS
};
The user can use macros to synchronize access to an object of the class.
void SomeMethod()
{
SomeClass obj;
LOCK_OBJECT( obj_lock, obj );
}
void SomeClass::SomeMethod()
{
LOCK_THIS_OBJECT( obj_lock );
}
If a critical section ends before the end of the scope, the UNLOCK
macro can be used to unlock the critical section object.
void SomeClass::SomeMethod()
{
LOCK_THIS_OBJECT( obj_lock );
UNLOCK( obj_lock );
LOCK( obj_lock );
}
Locking a critical section is possible without using GaSectionLock
objects. The LOCK
and UNLOCK
macros can be used directly on critical section objects.
GaCriticalSection _criticalSection;
void SomeMethod()
{
LOCK( _criticalSection );
UNLOCK( _criticalSection );
}
Catalogues are used to store available genetic operations. Each type of genetic operation has its own catalogue. Genetic operations are stored in a catalogue as a pair [operation name and pointer to the operation object]. The name of the operation must be unique in the catalogue. The user can obtain a pointer to the operation object (functors) by specifying the operation name. The GaCatalogue
template class manages the catalogues.
Diagram - GaCatalogue
class
The GaCatalogueEntry
class is used to store pointers to genetic operation objects and the name under which it is registered in the catalogue.
The Register
and Unregister
methods add or remove genetic operations from a catalogue. The GetEntryData
method finds genetic operations by their names.
The Genetic Algorithm Library defines eight built-in catalogues:
GaCrossoverCatalogue
GaMutationCatalogue
GaFitnessComparatorCatalogue
GaSelectionCatalogue
GaCouplingCatalogue
GaReplacementCatalogue
GaScalingCatalogue
GaStopCriteriaCatalogue
Before a catalogue for specific types of operations can be used, the MakeInstance
method must be called. FreeInstance
should be called after the catalogue is no loner needed. For built-in catalogues, these methods are called during the initialization and the finalization of the library. For user-defined catalogues, the user must manually call these methods.
GaSelectionOperation* select =
GaSelectionCatalgoue::Instance().GetEntryData(
"GaRandomSelect" );
GaCatalgoue<UserOperationType>::MakeInstance();
GaCatalgoue<UserOperationType>::Instance().Register(
"UserOperation1", new UserOperation1() );
GaCatalgoue<UserOperationType>::Instance().Register(
"UserOperation2", new UserOperation2() );
UserOperationType* operation =
GaCatalgoue<UserOperationType>::Instance().GetEntryData(
"UserOperation1" );
GaCatalgoue<UserOperationType>::Instance().Unregister(
"UserOperation1");
GaCatalgoue<UserOperationType>::FreeIntance();
Catalogues manage the memory used by objects that are registered.
The GaRandomGenerator
class implements the RANROT algorithm for generating random numbers. It generates a 64-bit wide integer [two 32-bit integers] at a time. All other built-in random number generators in the library are built on top of this class.
Data type | Interval | Class name | Global object |
int | 0-2147483647 | GaRandomInteger | GaGlobalRandomIntegerGenerator |
float | (0, 1) | GaRandomFloat | GaGlobalRandomFloatGenerator |
double | (0, 1) | GaRandomDouble | GaGlobalRandomDoubleGenerator |
bool | true or false | GaRandomBool | GaGlobalRandomBoolGenerator |
Diagram - Random number generators
These random number generator classes have methods that generate numbers in a predefined interval, between a predefined minimum and a user-defined maximum and a between user-defined minimum and maximum. GaRandomBool
has two additional methods that specify the probability of the true
value being generated.
The Genetic Algorithm Library defines a few interfaces that enable chromosomes to be used with built-in crossover and mutation operations.
The GaMutableCode
interface should be implemented by chromosomes that support random flipping or inverting of values in its code. This interface defines two methods: Invert
and Flip
. The Invert
method should choose a defined number of chromosome's code values and invert them. The Flip
method should change a randomly defined number of chromosome's code values.
Diagram - GaMutableCode
interface
The GaSwapableCode
interface should be implemented by chromosomes that can swap a block of chromosome's code values. This interface defines the Swap
method.
Diagram - GaSwapableCode
interface
The GaSizableCode
interface should be implemented by chromosomes that can change the number of values in its code. The Insert
method should insert a defined number of random values at a specified position in the chromosome's code. The Remove
method should remove a block of values at a specified position from the chromosome's code.
Diagram - GaSizableCode
interface
The GaMultiValueCode
interface should be implemented by chromosomes that can have more then one value in its code. This interface defines methods for extracting values from the chromosome's code into buffers and the initialization of chromosomes from buffers of values. The MakeBuffer
method should make a buffer object that can store a specified number of values and return a pointer to the object. FillBuffer
should copy a block of values at a specified position to a buffer. The FromBuffer
method should initialize a chromosome's code with values stored in a provided buffer.
Diagram - GaMultiValueCode
interface
The GaCodeValuesBuffer
class is used to manage buffers for storing values from chromosomes' codes. A buffer can be used to store values from multiple chromosomes so it is suitable for combining chromosomes in crossover operations.
Diagram - GaCodeValuesBuffer
class
The GaArithmeticalCode
interface should be implemented by chromosomes that support arithmetic operations over values in its code. This interface defines operators for basic arithmetic operations [+, -, *, /] and a method for finding the midpoint.
Diagram - GaArithmeticalCode
interface
The GaCodeValue
interface should be implemented by classes that wrap data types of values in a chromosome's code. This interface defines the FormBuffer
method that should extract a single value [at a specified position] from a provided buffer. The Initialize
method, when implemented, should randomly initialize a wrapped value.
Diagram - GaCodeValue
interface
In most situations, values in a chromosome's code have constraints [domain]. These constraints in the library are realized via value sets. The GaDomainChromosome
class uses a CCB that stores a pointer to a value set that defines the constraints of values in a chromosome's code.
Diagram - GaDomainChromosome
and GaChromosomeDomainBlock
classes
The GaValuSet
is a base class for value sets in the library.
Diagram - GaValueSet
class
The GenerateRandom
method randomly chooses one value from a value set and returns it. The Inverse
method returns the corresponding value from a group of inverted values. The Belongs
method returns true
if a specified value belongs to a value set. The ClosestValue
returns the closest value to a specified value which belongs to a value set.
Each value set has a group of values [called originals] and a corresponding group of opposite values [called inverted values]. The _viceVersa
member defines the treatment of the inverted values. If it is set to true
, the inverted values are treated as valid members of the value set, which means the inverted values can be used with all the defined operations of the value set in the same way as the original values.
The GaSingleValueSet
template class represents the value set with only one original value and one inverted value. This value set is useful for values of the chromosome's code that can have two possible states.
Diagram - GaSingleValueSet
class
The GaMultiValueSet
template class represents a value set with multiple values. Each value in a group of originals has exactly one corresponding inverted value. Duplicates of original values are not allowed. Inverted values can be duplicated only if _viceVersa
is set to false
.
Diagram - GaMultiValueSet
class
The GaIntervalValueSet
template class represents a value set that contains continues values in a specified interval. The bounds of the interval are defined by the GaValueIntervalBounds
template class. Type of values in the set must have the +
, -
, >
, >=
, <
, and <=
operators defined. The user must provide a generator of random values that implements the GaRandom
interface.
Diagram - GaIntervalValueSet
class
The GaUnboundValueSet
template class should be used when values of a chromosome's code has no additional constraints. The types of the stored values must have the unary -
operator defined. The user must provide a generator of random values that implements the GaRandom
interface. The range of values generated by this value set is determined only by the provided random generator.
Diagram - GaUnboundValueSet
class
GaCombinedValueSet
can be used to merge two or more value sets into one set.
Diagram - GaCombinedValueSet
class
The GaSingleValueChromosome
template class can be used for chromosomes representing a solution with a single value. It can be a single real or integer number or a value of any other type. This class implements only the GaMutableCode
interface. Because SingleValueChromosome
inherits the GaDomainChromosome
class, the domain of the value can be controlled by a value set.
The GaSVAChromosome
template class is suitable for chromosomes that support arithmetic operations because it implements GaArithmeticalCode
. The data type of the chromosome's values must have the +
, -
, *
, /
operators the and operator /
with a right-hand side operand of integer type defined. This allows chromosomes to be used with built-in arithmetic crossover operations.
Diagram - GaSingleValueChromosome
class
The GaMultiValueValueChromosome
template class can be used for chromosomes which require multiple values to represent a solution. This class implements the GaMutableCode
, GaSwapableCode
, GaSizableCode
, and the GaMultiValueCode
interfaces. The GaChromosomeValue
class is used for injecting values into a chromosome's code and extracting a value.
The GaMVAChromosome
template class extends GaMultiValueValueChromosome
in the same way that GaSVAChromosome
extends GaSingleValueValueChromosome
.
Diagram - GaMultiValueChromosome
class
The GaBinaryChromosome
template class implements chromosomes that use binary encoding to present a solution. This class has a set of methods that encode built-in types to a binary stream and that decode the stream into the original values. GaBinaryChromosome
uses the GaBinaryChromosomeParams
class for parameters of the chromosomes. This class defines a probability set state when the chromosome is created.
The GaBit
class is used for injecting bits into a chromosome's code or for extracting them.
Diagram - GaBinaryChromosome
class
These classes are located in the Chromosome::Representation
namespaces.
Built-in fitness comparators are located in the Chromosome::FitnessComparators
namespace.
Diagram - Built-in fitness comparators
There are two fitness comparators:
GaMaxFitnessComparator
- used for genetic algorithms which maximize the fitness value, GaMinFitnessComparator
- used for genetic algorithms which minimize the fitness value.
Built-in crossover operations are located in the Chromosome::Crossover
namespace.
Diagram - Built-in crossover operations
The GaMutiValueCrossover
class implements a crossover operation which creates a child by choosing N cross points, and then it copies values from parents in turns, changing the source parent each time it reaches a chosen cross point. This operation requires chromosomes that implement the GaMultiValueCode
interface.
Diagram - Results of GaMutiValueCrossover
operation
The GaMidpointCrossover
class implements a crossover operation which creates a child by invoking the Midpoint
method of the chosen parents. This operation requires chromosomes that implement the GaArithmeticalCode
interface.
Diagram - Results of GaMidpointCrossover
operation [multi-value chromosome, type of values is int
]
GaAddCrossover
invokes operator+
of the parent chromosome and GaSubCrossover
invokes operator-
.
Diagram - Results of GaAddCrossover
operation [multi-value chromosome, type of values is int
]
Diagram - Results of GaSubCrossover
operation [multi-value chromosome, type of values is int
]
Built-in crossover operations are located in the Chromosome::Mutation
namespace.
Diagram - Built-in mutation operations
The GaSwapMutation
class implements a mutation operation which chooses two blocks of values in a chromosome's code and swaps their positions in the code [the maximal number of values that are swapped is defined by the mutation size specified in the parameters of the chromosome].
Diagram - Results of GaSwapMutation
operation
The GaInvertMutation
class implements a mutation operation which chooses N values [the maximal number of values is defined by the mutation size specified in the parameters of the chromosome] and invert their values using the Invert
method of the value set defined by the chromosome. This operation requires chromosomes that implement the GaMutableCode
interface.
Diagram - Results of GaInvertMutation
operation
The GaFlipMutation
class implements a mutation operation which chooses N values [the maximal number of values is defined by the mutation size specified in the parameters of the chromosome] and sets their values randomly using the GenerateRandom
method of the value set defined by the chromosome. This operation requires chromosomes that implement the GaMutableCode
interface.
Diagram - Results of GaFlipMutation
operation
Built-in Selection Operations
Built-in selection operations are located in the Population::SelectionOperations
namespace.
Diagram - GaSelectBest
and GaSelectWorst
selection operations
The GaSelectBest
class implements a selection operation that selects N [defined by the selection size in the parameters of the operation] chromosomes which are the best in the population. If the population is not sorted, the operation can only select those chromosomes that are in the best chromosomes sorted group of the population.
GaSelectRouletteWheel
and GaSelectTournament
operations
The GaSelectRouletteWheel
class implements a selection operation that selects chromosomes based on their fitness value. Fitness values of the chromosomes are used to calculate their probability of selection. When the genetic algorithm maximizes fitness, greater fitness means greater selection probability. If the genetic algorithm minimizes fitness, lower fitness means greater selection probability. The operation requires a sorted population. If the population has defined a scaling operation, the selection uses the scaled fitness values; otherwise, it uses the raw fitness values. This selection operation can select a single parent more the once, which can cause problems for some replacement operations. To avoid this, GaSelectRouletteWheel
uses the GaSelectDuplicatesParams
class for its parameters to control duplicates in the selection result set.
GaSelectTournament
uses a similar method as GaSelectRouletteWheel
. It performs N [defined by the parameters of the operation] "roulette wheel" selections for a single place in the selection result set. The best chromosome among the chosen is placed in the result set. This process is repeated to select all parents. The operation uses the GaSelectTournamentParam
class for its parameters.
Diagram - GaSelectRandom
and GaSelectRandomBest
operations
The GaSelectRandom
class implements a selection operation which chooses parents randomly. The operation can select a single parent more the once, which can cause problems for some replacement operations. To avoid this, GaSelectRandom
uses the GaSelectDuplicatesParams
class for its parameters to control duplicates in the selection result set.
GaSelectRandomBest
works the same way as GaSelectRandom
, but it selects more parents than is defined by the parameters; then, it truncates the result set so it can fit to the desired selection size, leaving only the best parents in the set. The GaRandomBestParams
class is used by this operation, and it defines the number of parents to select before the truncation.
Built-in coupling operations are located in the Population::CouplingOperations
namespace.
Diagram - Built-in coupling operations [1]
The GaSimpleCoupling
operation takes the first two parents from the selection result set and then produces two offspring chromosomes using crossover operations, and each parent is bound to a child, then it takes next two parents, and so on... If all parents have been used, but more children should be produced, the operation restarts from the beginning of the selection result set until enough children are produced. This coupling uses the GaCouplingParams
class for its parameters.
Diagram - GaSimpleCoupling
operation
Diagram - Built-in coupling operations [2]
The GaCrossCoupling
operation takes parents sequentially from the selection result set. If all the parents have been used, but more children should be produced, the operation restarts from the beginning until enough children are produced.
Diagram - GaCrossCoupling
operation
The GaInverseCoupling
operation takes the first parents sequentially from the selection results, and the second parents are the ones who are at a distance from the last chromosome in the selection results which is equal to the distance of the first parent from the first chromosome in the result set. If all parents have been used, but more children should be produced, the operation restarts from the beginning until enough children is produced.
Diagram - GaInverseCoupling
operation
The GaRandomCoupling
operation takes the first parents sequentially from the selection result set, and the second parents are chosen randomly. If all parents have been used as first parents, but more children should be produced, the operation restarts from the beginning until enough children is produced.
Diagram - GaRandomCoupling
operation
GaBestAlwaysCoupling
operation always takes chromosome with the best fitness value in the selection result set for the first parent and the second parents are sequentially taken. If all parents have been used, but more children should be produced, the operation restarts from the beginning until enough children is produced.
Diagram - GaBestAlwaysCoupling
operation
When two parents are chosen, these operations produce a specified number of children using a crossover operation. Then, they choose a child with the best fitness value among the produced children, stores the child in the coupling result set, and bounds a parent to the child. These couplings use the GaMultipleCrossoverCouplingParams
class for parameters to control the number of produced children per parent's pair.
Built-in replacement operations are located in the Population::ReplacementOperations
namespace.
Diagram - GaReplaceWorst
and GaReplacBest
operations
The GaReplaceWorst
operation replaces the worst chromosomes in the population with offspring chromosomes form the provided coupling result set. If the population is not sorted, the operation can replace only those chromosomes which are stored in the worst sorted group of the population. This operation uses the GaReplacementParams
class for its parameters.
The GaReplaceBest
operation works the same way as GaReplaceWorst
, but it replaces the best chromosomes in the population.
Diagram - GaReplaceParents
and GaReplaceRandom
operations
The GaReplaceParents
operation replaces the parents of the offspring chromosomes in the provided coupling result set. As mentioned, the coupling operation stores information about a chromosome's parent in the coupling result set. If this operation is used, the selection operation should not select the same chromosome more then once and the coupling operation should not bound one parent to more than one child.
The GaReplaceRandom
operation randomly chooses chromosomes which are going to be replaced.
These two operations can remove the best chromosomes from the population; to prevent this, they implement an elitism mechanism. The GaReplaceElitismParams
class is used by the operations and defines the number of the best chromosomes in the population that are safe from the replacement operation.
Built-in scaling operations are located in the Population::ScalingOperations
namespace.
Diagram - GaWindowScaling
and GaNormalizationScaling
operations
The GaWindowScaling
operation calculates the scaled fitness by subtracting the fitness of the worst chromosome form the fitness of the chromosome which is scaled [scaled_fitness = chromosome_fitness - worst_chromosome_fitness
].
The GaNoramlizationScaling
operation calculates the scaled fitness based on the ranking of the chromosome [scaled_fitness = population_size - chromosome_rank
]. It requires a sorted population.
These operations do not require parameters.
Diagram - GaLinearScaling
and GaExponentialScaling
operations
The GaLinearScaling
operation scales the fitness values using linear transformation: scaled_fitness = a * fitness + b
[a
and b
are scaling coefficients]. a
and b
are calculated in the following manner:
if( min_f > ( factor * avg_f - max_f ) / factor - 1 )
{
a = avg_f / ( avg_f - min_f );
b = -1 * min_f * avg_f / ( avg_f - min_f );
}
else
{
a = ( factor - 1 ) * avg_f / ( max_f - avg_f );
b = avg_f * ( max_f - factor * avg_f ) / ( max_f - avg_f );
}
min_f
- fitness value of the worst chromosome in the population max_f
- fitness value of the best chromosome in the population avg_f
- average fitness value of all chromosomes in the population factor
- scaling factor [used to multiply the average fitness which determines the scaled fitness value of the best chromosome in the population]
This operation cannot work with negative fitness values.
The GaExponentialScaling
operation calculates the scaled fitness by raising a chromosome's fitness value to a specified power [specified by the parameters of the population].
Both operations use the GaScaleFactorParams
class for their parameters.
Built-in stop criteria operations are located in the Population::StopCriterias
namespace.
Diagram - GaGenerationCriteria
and GaGenerationCriteriaParams
classes
GaGenerationCriteria
is used to stop the genetic algorithm when it reaches a desired number of generations. It uses the GaGenerationCriteriaParams
class as parameters.
Diagram - GaFitnessCriteria
and GaFitnessCriteriaParams
classes
GaFitnessCriteria
decides when the algorithm should stop based on the algorithm's statistical information of fitness values [raw or scaled, such as fitness of the best chromosome, average fitness, or the fitness of the worst chromosome]. The GaFitnessCriteriaComparison
enumeration defines the types of comparisons that can be used to compare the desired fitness value with a specified limit. GaFitnessCriteria
uses the GaFitnessCriteriaParams
class as its parameters.
Diagram - GaFitnessProgressCriteria
and GaFitnessProgressCriteriaParams
classes
GaFitnessProgressCriteria
decides when the algorithm should stop based on the progress of the algorithm's statistical information of fitness values. This criteria measures and tracks the progress of the desired value during the generations of the algorithm. If the algorithm fails to make the desired progress in a single generation after a defined number of generations, the criteria instructs the algorithm to stop. The GaFitnessProgressCriteriaParams
class represents the parameters for this criteria. The parameters' class also stores the history of the algorithm's progress.
Built-in genetic algorithms are located in the Population::SimpleAlgorithms
namespace.
Diagram - Built-in genetic algorithms
The GaSimplemAlgorithm
class implements a genetic algorithm with non-overlapping populations. The user needs to supply a population object [does not have to be initialized] when creating the genetic algorithm. The algorithm implements elitism, and a number of saved chromosomes are defined by the parameters of the algorithm [the GaSimpleAlgorithmParams
class]. The algorithm works in the following manner:
- create copy of supplied population object
- initialize provided population object
- select parents and produce offspring chromosomes
- copy the best chromosome from the current population [elitism]
- insert offspring chromosomes into new population
- check stop criteria [exit if reached]
- switch population objects and return to step 3.
The GaIncrementalAlgorithm
class implements a genetic algorithm that uses an overlapping population and replaces only a few chromosomes per generation [using a replacement operation]. The user needs to supply a population object [does not have to be initialized] when creating the genetic algorithm. The algorithm works in the following manner:
- initialize provided population object
- select parents and produce offspring chromosomes
- replace old chromosomes with offspring
- check stop criteria [exit if reached]
- switch population object and return to step 2.
The user must perform these steps to build a genetic algorithm with this library:
- Choose representation of the chromosomes
- Define fitness operation
- Choose crossover and mutation operations and fitness comparator
- Choose selection, coupling, replacement, and scaling operations
- Choose type of algorithm and stop criteria
Important: before using the GAL, GaInitialize
must be called. Also, before quitting the application, GaFinalize
must be called.
The easiest way is to choose a multi-value chromosome's representation which supports arithmetic operations [the Chromosome::Representation::GaMVArithmeticChromosome<double>
class].
After choosing a chromosome's representation, the user must define the fitness operation.
class fFitness : public GaFitnessOperation
{
public:
virtual float GACALL operator() (const GaChromosome*
chromosome) const
{
const vector<double>& vals=
dynamic_cast<const GaMVArithmeticChromosome<double>*>
(chromosome)->GetCode();
return 5*vals[0]*sin(vals[0])+1.1*vals[1]*sin(vals[1]);
}
virtual GaParameters* GACALL MakeParameters() const { return NULL; }
virtual bool GACALL CheckParameters(const GaParameters&
parameters) const { return true; }
};
The fFitness
class inherits the GaFitnessOperation
class and overrides operator()
which calculates the fitness value of the chromosome by evaluating the actual mathematical function.
The next step is to build a chromosome configuration block (CCB) which contains:
- pointer to parameters of chromosomes
- pointer to genetic operation functors [crossover, mutation, fitness operation, and fitness comparator]
- pointer to value set which defines the domain of
x
and y
variables
The class Chromosome::Representation::GaValueInterval<T>
is used as the chromosome's value set because the domain of x
and y
variables is a continuous interval (0, 10). GaIntervalValueSet
requires four bounds [low and high bounds to specify the interval of the original values, and low and high bounds to specify the interval of the inverted values] and a generator of random values.
GaValueIntervalBound<double /> valueInt(0,10);
GaValueIntervalBound<double /> invValueInt(0,10);
GaValueInterval<double /> valueSet(valueInt,invValueInt,
GaGlobalRandomDoubleGenerator,false);
The CCB should be:
fFitness fitnessOperation;
GaChromosomeDomainBlock<double> configBlock(&valueSet,
GaCrossoverCatalogue::Instance().GetEntryData(
"GaMultiValueCrossover"),
GaMutationCatalogue::Instance().GetEntryData("GaFlipMutation"),
&fitnessOperation,
GaFitnessComparatorCatalogue::Instance().GetEntryData(
"GaMinFitnessComparator"),
&chromosomeParams);
The CCB is defined to use the GaMultiValuesCrossover
and GaFlipMutation
operations. GaMinFitnessComparator
is specified because the purpose of the algorithm is to find the minimum of the function.
When the CCB is defined, the user can build the prototype chromosome:
GaMVArithmeticChromosome<double> prototype(2,&configBlock);
Besides the prototype chromosome, the user must define the population's parameters before the population object can be created:
- population size: 30
- resizable population: no [incremental algorithm is used, which does not require resizable population]
- population is sorted: yes
- scaled fitness is used for sorting: no
- tracking of the best chromosomes: 0 [population is already sorted]
- tracking of the worst chromosomes: 0 [population is already sorted]
GaPopulationParameters populationParams(30,false,true,false,0,0);
This population object uses the default configuration, except it changes the sort comparator:
- selection operations:
GaSelectRouletteWheel
- number of selected chromosomes: 2
- coupling operation:
GaInverseCoupling
- offspring produced: 2
- replacement operation:
GaReplaceWorst
- chromosomes replaced: 2
- scaling operation: none
- sort comparator:
GaMaxFitnessComparator
[default] changed to GaMinFitnessComparator
Everything is now ready to create the population object:
GaPopulationConfiguration populationConfig;
populationConfig.SetParameters(populationParams);
populationConfig.SetSortComparator(
&configBlock.GetFitnessComparator());
GaPopulation population( &prototype, &populationConfig );
This example uses an incremental genetic algorithm [the GaIncrementalAlgorithm
class]. To create the algorithm's object:
GaMultithreadingAlgorithmParams algorithmParams(1);
GaIncrementalAlgorithm algorithm(&population,algorithmParams);
where the user specifies the population on which the genetic algorithm will operate and the parameters of the algorithm. The constructor of the algorithm's parameters takes the number of working threads.
When the user builds a genetic algorithm for these kind of problems, it is not possible to know the exact termination criteria of the algorithm. In these situations, it is convenient to use a stop criteria based on the duration of the evolution process or its progress. One such stop criteria is a criteria based on the number of generations. The example uses only one thread because the algorithm produces only a few new chromosomes per generation.
GaGenerationCriteriaParams criteriaParams(100000);
algorithm.SetStopCriteria(
GaStopCriteriaCatalogue::Instance().GetEntryData(
"GaGenerationCriteria"),&criteriaParams);
The constructor of the criteria's parameters takes the number of generations after which the algorithm should stop.
To monitor the evolution process, the user must specify an observer object to the genetic algorithm.
class fObserver : public GaObserverAdapter
{
virtual void GACALL NewBestChromosome(const GaChromosome&
newChromosome,const GaAlgorithm& algorithm)
{
const vector<double>& vals=
dynamic_cast<const GaMVArithmeticChromosome<double>&>
(newChromosome).GetCode();
cout << "New chromosome found:\n";
cout << "Fitness: " << newChromosome.GetFitness() << endl;
cout << "x: " << vals[0] << " y: " << vals[1] << endl;
}
virtual void GACALL EvolutionStateChanged(GaAlgorithmState
newState,const GaAlgorithm& algorithm)
{
if(newState==GAS_RUNNING)
cout << "start\n";
else if(newState==GAS_CRITERIA_STOPPED)
cout << "end";
}
};
To register the observer:
fObserver observer;
algorithm.SubscribeObserver(&observer);
And to start the algorithm:
algorithm.StartSolving(false);
StartSolving
's parameter defines whether the algorithm should continue a previously paused evolution process [true
] or it should start an entirely new process [false
].
Screenshot - Pattern Test application
This example implements a genetic algorithm that tries to guess the sequence of characters. The example defines this sequence of characters:
const char pattern[] =
" GGGGGGGGGGGGG AAA LLLLLLLLLLL "
" GGG::::::::::::G A:::A L:::::::::L "
" GG:::::::::::::::G A:::::A L:::::::::L "
" G:::::GGGGGGGG::::G A:::::::A LL:::::::LL "
" G:::::G GGGGGG A:::::::::A L:::::L "
"G:::::G A:::::A:::::A L:::::L "
"G:::::G A:::::A A:::::A L:::::L "
"G:::::G GGGGGGGGGG A:::::A A:::::A L:::::L "
"G:::::G G::::::::G A:::::A A:::::A L:::::L "
"G:::::G GGGGG::::G A:::::AAAAAAAAA:::::A L:::::L "
"G:::::G G::::G A:::::::::::::::::::::A L:::::L "
" G:::::G G::::G A:::::AAAAAAAAAAAAA:::::A L:::::L LLLLLL"
" G:::::GGGGGGGG::::G A:::::A A:::::A LL:::::::LLLLLLLLL:::::L"
" GG:::::::::::::::G A:::::A A:::::A L::::::::::::::::::::::L"
" GGG::::::GGG:::G A:::::A A:::::A L::::::::::::::::::::::L"
" GGGGGG GGGGAAAAAAA AAAAAAALLLLLLLLLLLLLLLLLLLLLLLL";
const int patternSize=sizeof(pattern)-1;
UThe ued symbols are: G,A,L,: and a white space.
The genetic algorithm uses a Chromosome::Representation::GaMVArithmeticChromosome<double>
chromosome representation with a defined domain of values by the Chromosome::Representation::GaMultiValueSet<char>
class.
GaMultiValueSet<char> valueSet(false);
valueSet.Add("GAL: "," ",5);
The fitness operation calculates the percent of matched characters and returns that number as the fitness value of the chromosomes:
class pFitness : public GaFitnessOperation
{
public:
virtual float GACALL operator()(const GaChromosome*
chromosome) const
{
const vector<char>& v=
dynamic_cast<const GaMultiValueChromosome<char>*>
(chromosome)->GetCode();
int score=0;
for(int i=0;i<patternSize;i++)
{
if(v[i]==pattern[i])
score++;
}
return (float)score/patternSize*100;
}
virtual GaParameters* GACALL MakeParameters() const
{ return NULL; }
virtual bool GACALL CheckParameters(const GaParameters&
parameters) const { return true; }
};
The CCB looks like the CCB in the previous example, except it uses a new fitness operation and another fitness comparator, because its objective now is to maximize the fitness:
pFitness fitnessOperation;
GaChromosomeDomainBlock<char /> configBlock(&valueSet,
GaCrossoverCatalogue::Instance().GetEntryData(
"GaMultiValueCrossover"),
GaMutationCatalogue::Instance().GetEntryData("GaFlipMutation"),
&fitnessOperation,
GaFitnessComparatorCatalogue::Instance().GetEntryData(
"GaMaxFitnessComparator"),
&chromosomeParams);
Prototype chromosome:
GaMultiValueChromosome<char> prototype( patternSize, &configBlock );</char>
This example uses a genetic algorithm with non-overlapping populations, and it produces the entire population. To increase the diversity of the produced chromosomes, the number of selected chromosomes is increased. Note that this type of an algorithm requires a resizable population. The population object and its configuration:
GaPopulationParameters populationParams(30,true,true,false,0,0);
GaPopulationConfiguration populationConfig;
populationConfig.SetParameters(populationParams);
populationConfig.SetSortComparator(
&configBlock.GetFitnessComparator());
populationConfig.Selection().GetParameters().SetSelectionSize(6);
GaPopulation population(&prototype, &populationConfig);
As mentioned, this example uses the Algorithm::SimpleAlgorithms::GaSimpleAlgorithm
class for the genetic algorithm.
GaSimpleAlgorithmParams algorithmParams(10,2);
GaSimpleAlgorithm algorithm(&population,algorithmParams);
The first argument of the parameters' constructor is the elitism depth and the second is the number of working threads. This algorithm produces much more chromosomes per generation than the previous one, so it is suitable for parallelization.
In this example, the exact termination condition is known: when the algorithm finds the chromosome with a fitness value of 100 [100% match]. The right stop criteria is Algorithm::StopCriterias::GaFitnessCriteria
:
GaFitnessCriteriaParams criteriaParams(100,GFC_EQUALS_TO,
GSV_BEST_FITNESS);
algorithm.SetStopCriteria(
GaStopCriteriaCatalogue::Instance().GetEntryData(
"GaFitnessCriteria"),
&criteriaParams);
The observer of the algorithm displays the best chromosomes as they are found:
class pObserver : public GaObserverAdapter
{
public:
virtual void GACALL NewBestChromosome(const GaChromosome&
newChromosome,const GaAlgorithm& algorithm)
{
const vector<char>& v=
dynamic_cast<const GaMultiValueChromosome<char>&>
(newChromosome).GetCode();
cout<<"Generatiron: "<<
algorithm.GetAlgorithmStatistics().GetCurrentGeneration()
<<endl;
cout<<"Fitness: "<<newChromosome.GetFitness();
cout<<"\n-------------------------\n";
for(int i=0;i<v.size();i++)
{
if(!(i%78))
cout<<endl;
cout<<v[i];
}
cout<<"\n-------------------------\n";
}
virtual void GACALL EvolutionStateChanged(GaAlgorithmState
newState,const GaAlgorithm& algorithm)
{
if(newState==GAS_CRITERIA_STOPPED)
cout<<"end.";
}
};
The subscription of the observer is the same as in the previous example:
pObserver observer;
algorithm.SubscribeObserver( &observer );
The starting of the evolution:
algorithm.StartSolving(false);
Screenshot - TSP application
The chromosome is an array of cities [pointers to objects of the TspCity
class] in the order in which they are visited. It is implemented by the TspChromosome
class. The class inherits GaMultiValueChromosome
to implement a custom initialization of the chromosome by overriding the MakeFromPrototype
method. This method copies cities into the chromosomes' code and then it shuffles their positions. This class also overrides the MakeCopy
method and defines a copy constructor.
class TspChromosome : public GaMultiValueChromosome<const TspCity*>
{
public:
TspChromosome(GaChromosomeDomainBlock<const TspCity*>* configBlock) :
GaMultiValueChromosome(configBlock) { }
TspChromosome(const TspChromosome& chromosome,
bool setupOnly) :
GaMultiValueChromosome<const TspCity*>(chromosome, setupOnly) { }
virtual GaChromosomePtr GACALL MakeCopy(bool setupOnly) const
{ return new TspChromosome( *this, setupOnly ); }
virtual GaChromosomePtr GACALL MakeNewFromPrototype() const;
int GACALL GetCityPosition(const TspCity* city) const;
};
Using a simple single-point or multi-point crossover operation will generate a large amount of invalid solutions which degrades the algorithm's performance and results. To prevent the generation of invalid solutions, the algorithm uses a custom crossover operation. The operation takes a random city from one parent and copies it to the child chromosome. Then, it searches for the cities which are connected to the chosen city [in both parents] and takes the nearest one [and copies it to the child chromosome] if it is not already taken. It is taken if the operation chooses another connected city. If all the connected cities are taken, the operation randomly chooses a city that has not been taken. Then, the crossover uses that city to extend the path in the same way. The process is repeated to select all the cities. The TspCrossover
class implements this crossover operation:
class TspCrossover : public GaCrossoverOperation
{
public:
virtual GaChromosomePtr GACALL operator ()(
const GaChromosome* parent1,
const GaChromosome* parent2) const;
virtual GaParameters* GACALL MakeParameters() const { return NULL; }
virtual bool GACALL CheckParameters(
const GaParameters& parameters) const { return true; }
private:
inline void SelectNextCity(const TspCity* previousCity,
const TspCity** currentBestNextCity,
const TspCity* nextCity) const;
};
The algorithm uses the built-in GaSwapMutation
operation. The fitness value is equal to the length of the path. The TspFitnessOperation
class implements the fitness operation:
class TspFitness : public GaFitnessOperation
{
public:
virtual float GACALL operator ()(
const GaChromosome* chromosome) const;
virtual GaParameters* GACALL MakeParameters() const { return NULL; }
virtual bool GACALL CheckParameters(
const GaParameters& parameters) const { return true; }
};
Parameters of chromosomes:
- mutation probability: 3%
- mutation size: 2
- improving only mutations: no
- crossover probability: 80%
- number of crossover points: 1 [ignored]
CCB:
TspCrossover
TspSwapMutation
TspFitnessOperation
TspMinFitnessComparator
- Value set is not defined
Population parameters:
- population size: 100
- resizable population: no [an incremental algorithm is used which does not require a resizable population]
- population is sorted: yes
- scaled fitness is used for sorting: no
- tracking of the best chromosomes: 0 [population is already sorted]
- tracking of the worst chromosomes: 0 [population is already sorted]
Configuration of the population:
GaSelectRandomBest
selection which selects 8 chromosomes GaSimpleCoupling
which produces 8 offspring chromosomes GaRandomReplaces
which replaces 8 chromosomes in each generation, with an elitism size of 10 chromosomes - No scaling operation
The algorithm uses GaFitnessProgressCriteria
because the exact termination condition is not known. The criteria will stop the algorithm if it is unable to improve the fitness value for more than 1 in 50000 generations. The genetic algorithm is incremental.
The TSP
class is the container for the object of the algorithm. The TspCity
class represents and stores information about a city [such as its coordinates and name]. It has the GetDistance
method which calculates the distances between the cities. TspCities
manages the collection of cities entered by the user.
Screenshot - Class Schedule application
The genetic algorithm for making the class schedule is already described here. In this article, the demo application demonstrates the solution of the same problem using the Genetic Algorithm Library.
The Schedule
class defines a chromosome's representation. It inherits GaMultiValueChromosome
.
class Schedule : public GaMultiValueChromosome<list<CourseClass*> >
{
friend class ScheduleCrossover;
friend class ScheduleMutation;
friend class ScheduleFitness;
friend class ScheduleObserver;
private:
CourseClassHashMap _classes;
CourseClassHashMap _backupClasses;
mutable vector<bool> _criteria;
public:
Schedule(GaChromosomeDomainBlock<list<CourseClass*> >* configBlock);
Schedule(const Schedule& c, bool setupOnly);
virtual ~Schedule() { }
virtual GaChromosomePtr GACALL MakeCopy(bool setupOnly) const
{ return new Schedule( *this, setupOnly ); }
virtual GaChromosomePtr GACALL MakeNewFromPrototype() const;
virtual void GACALL PreapareForMutation();
virtual void GACALL AcceptMutation();
virtual void GACALL RejectMutation();
inline const hash_map<courseclass*, />& GetClasses() const
{ return _classes; }
inline const vector<bool>& GetCriteria() const
{ return _criteria; }
inline const vector<list<CourseClass*>>& GetSlots() const
{ return _values; }
};
Then the crossover, mutation, and fitness operations are defined:
class ScheduleCrossover : public GaCrossoverOperation
{
public:
virtual GaChromosomePtr GACALL operator ()(
const GaChromosome* parent1,
const GaChromosome* parent2) const;
virtual GaParameters* GACALL MakeParameters() const
{ return NULL; }
virtual bool GACALL CheckParameters(
const GaParameters& parameters) const { return true; }
};
class ScheduleMutation : public GaMutationOperation
{
public:
virtual void GACALL operator ()(
GaChromosome* chromosome) const;
virtual GaParameters* GACALL MakeParameters() const
{ return NULL; }
virtual bool GACALL CheckParameters(
const GaParameters& parameters) const { return true; }
};
class ScheduleFitness : public GaFitnessOperation
{
public:
virtual float GACALL operator ()(
const GaChromosome* chromosome) const;
virtual GaParameters* GACALL MakeParameters() const
{ return NULL; }
virtual bool GACALL CheckParameters(
const GaParameters& parameters) const { return true; }
};
For more information about the chromosome representation and these operations, see this article.
The ScheduleTest
class is the container for the objects of the genetic algorithm.
ScheduleTest::ScheduleTest()
{
GaInitialize();
_chromosomeParams = new GaChromosomeParams(
0.03F, 2, false, 0.8F, 2 );
_ccb = new GaChromosomeDomainBlock<list<courseclass* /> >(
NULL, &_crossoverOperation, &_mutationOperation,
&_fitnessOperation,
GaFitnessComparatorCatalogue::Instance().GetEntryData(
"GaMaxFitnessComparator" ),
_chromosomeParams );
_prototype = new Schedule( _ccb );
GaPopulationParameters populationParams(
100, false, true, false, 2, 0 );
GaSelectRandomBestParams selParam( 10, false, 16 );
GaReplaceElitismParams repParam( 10, 2 );
GaCouplingParams coupParam( 10 );
_populationConfig = new GaPopulationConfiguration( populationParams,
&_ccb->GetFitnessComparator(),
GaSelectionCatalogue::Instance().GetEntryData(
"GaSelectRandom" ), &selParam,
GaReplacementCatalogue::Instance().GetEntryData(
"GaReplaceRandom" ), &repParam,
GaCouplingCatalogue::Instance().GetEntryData(
"GaSimpleCoupling" ), &coupParam,
NULL, NULL );
_population = new GaPopulation( _prototype, _populationConfig );
GaMultithreadingAlgorithmParams algorithmParams( 2 );
_algorithm = new GaIncrementalAlgorithm(
_population, algorithmParams );
GaFitnessCriteriaParams criteriaParams(
1, GFC_EQUALS_TO, GSV_BEST_FITNESS );
_algorithm->SetStopCriteria(
GaStopCriteriaCatalogue::Instance().GetEntryData(
"GaFitnessCriteria" ), &criteriaParams );
_algorithm->SubscribeObserver( &_observer );
}
The Genetic Algorithm Library supports the following compilers and platforms:
| Microsoft C++ | Intel C++ | GCC G++ | Borland C++ | Sun Studio C++ |
Windows | 12
| 12
|
| 6
|
|
Linux |
| 34
| 34
|
|
|
Mac OS X |
| 34
| 34
|
|
|
*BSD |
|
| 345
|
|
|
Solaris |
|
| 5
|
| 8
|
| - Compiler is supported. |
| - Compiler is not supported. | 1 | - Available as Visual Studio project. | 2 | - Can be compiled as static or dynamic library (DLL). | 3 | - Makefile available. | 4 | - Can only be compiled as static library. | 5 | - gmake command is used for building the library. | 6 | - compiler must be configured to use the STLport library. | 7 | - dmake command is used for building the library. |
|
The library contains a set of preprocessor directives that control the compilation process according to the detected compiler and the targeted operating system.
The Genetic Algorithm Library is available in two versions of Visual Studio 2005 projects. The first one is configured to use the Microsoft C/C++ compiler and the second one uses the Intel C++ compiler. Projects are located in /vs directory.
To add the Genetic Algorithm Library functionality to the application, the library must be linked with it. There are two methods to do this in Visual Studio 2005:
The procedures are same for both versions of the project.
The library can be compiled as a static or dynamic [DLL] library. It is compiled as a DLL, by default; if it is compiled and used as a static library, GENETICLIBRARY_STATIC
must be defined.
Output files are GeneticLibrary.dll and GeneticLibrary.lib when the library is compiled as a DLL, or only GeneticLibrary.lib if it is compiled as a static library. These files are located in the /build/%configuration%/%compiler% directory, where %configuration% is debug or release, and %compiler% is msvc for the Microsoft C/C++ compiler, or icc_win for the Intel C++ compiler. The GeneticLibrary.dll file must be copied to the same directory where the executable file of the application resides.
The Genetic Algorithm Library is linked against the dynamic version of the common run-time libraries [CRT], by default. When the library is linked against the dynamic version of the CRT, the application may fail to start on machines which do no have the Microsoft Visual C++ 2005 Redistributable Package installed. It is important to notice that the application which uses the Genetic Algorithm Library must be linked against the same version CRT as the library.
The compilation of the library can be done from the console by invoking make with an appropriate Makefile. On the Solaris operating system, gmake is used for compiling the library with GCC G++ and dmake for compiling with Sun Studio C++. For *BSD systems, use GNU make [gmake] instead of BSD make [make].
make -f %compiler%_%platform%_%configuration% all
where %compiler% is:
- gcc - for GCC G++ compiler.
- icc - for Intel C++ compiler.
- scc - for Sun Studio C++ compiler.
%platform%s are the following:
- linux - for the Linux family of operating systems.
- macos - for the Mac OS X operating system.
- solaris - for the Solaris operating system.
- bsd - for the BSD family of operating systems.
and the configuration is one of these:
- debug - compiles the library with debugging information and no optimization.
- release - compiles the library with optimized code generation, and it strips the debugging information.
Makefiles are available in the /makefiles directory.
make -f icc_linux_debug all
Example: Compilation as Debug on Linux using Intel C++
gmake -f gcc_bsd_release all
Example - Compilation as Release on FreeBSD using GCC G++
The output file is a static library named libGeneticLibrary.a and it is located in the /build/%configuration%/%compiler%_%platform% directory.
To link the Genetic Algorithm Library with an application, the user must specify a path to the library and the name of the library file:
g++ -Wl,-L"%path_to_library%/build/%configuration%/%compiler%_%platform%"
-lGeneticLibrary -o "application_executable" [list of object files]
For the Intel C++ compiler, the user should use the icpc command instead of g++, and for the Sun Studio C++ compiler, the cc command.
%path_to_library% is the path to the directory where the library is located. On some platforms, there are additional requirements for linking the application with the Genetic Algorithm Library. On Linux, the -lrt switch must be specified to the linker. The Sun Studio linker requires the -library=stlport4 and -lrt switches, and the GNU linker on *BSD system requires the -march=i486 and -lpthread switches.
To port this library to other platforms with no major changes to the core of the library, the targeted platform must support:
- Multithreading - if the targeted platform has POSIX Threads support, porting can be easier because the Genetic Algorithm Library already employs Pthreads for multithreading on UNIX-like systems.
- Atomic increment, decrement operations as well as atomic compare and exchange instructions or atomic exchange operation.
- STL - The Genetic Algorithm Library relies, in some segments, on STL and some nonstandard STL extensions such as
hash_map
. - IEEE754 standard for floating point numbers - some parts of the library, like the random number generator, assumes that the targeted processor architecture supports this standard.
- Return value of
operator==
in GaChromosome
interface is now bool
. operator !=
is also added to GaChromosome
interface. This change also affects: GaMultiValueChromosome
, GaSingleValueChromosome
and GaBinaryChromosome
classes. - Random number generator algorithm has been changed from RANROT to MWC.
- Declaration of method
void GaRandomGenerator::Generate(unsigned int& word1, unsigned int& word2)
has been changed to int GaRandomGenerator::Generate()
. - Declaration of method
void GaRandomGenerator::Initalization(unsigned int seed)
has been changed to void GaRandomGenerator::Initalization(unsigned int seed1, unsigned int seed1)
. enum GaRandomParams
has been removed since it is no longer needed after algorithm change. struct GaState
has been added as a member of GaRandomGenerator
class and it represents current state of random number generator. Its members _w
and _z
store 64-bit state. - The way of generating random numbers in specified interval has been changed to equalize probabilities for all numbers in the interval. The maximal value specified to the
Generate
method is now included in interval. This change affects: GaRandomInt
, GaRandomBool
, GaRandomFloat
and GaRandomDouble
.
GaChromosomeDomainBlock
now can stores multiple value sets. const GaValueSet<t>* GACALL GetValueSet(int pos) const</t>
, void GACALL SetValueSet(GaValueSet<t>* domain, int pos)</t>
and int GACALL GetValueSetCount() const
method are added to the class. Declaration of method const T& GetClosestValue(const T& value) const
has been changed to const T& GetClosestValue(const T& value, int pos) const
. - Multivalue chromosome represented by
GaMultiValueChromosome
class now uses separate value set for each value position. This change also affects GaMVArithmeticChromosome
class. - Coupling operations now can check whether the produced offspring chromosome already exists in the population and not insert it to result set if that is the case. The operation stores this setting to result set, so replacement operation can clear duplicates before they are inserted in population. To accomplish this,
GetCheckForDuplicates
and SetCheckForDuplicates
methods have been added to GaCouplingParams
class and SetClearDuplicates
and GetClearDuplicates
methods to GaCouplingResultSet
class. This change also affects GaMulitpleCrossoverCouplingParams
. Checking is implemented by CheckForDuplicates
function. Production of offspring chromosomes for all built-in operations is now implemented in a single function: ProduceOffspring
.
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