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AForge.NET open source framework

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16 May 2007GPL311 min read 828.2K   48.3K   346  
The article describes an open source C# framework for researchers in the areas of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, etc.
// AForge Genetic Library
//
// Copyright � Andrew Kirillov, 2006
// andrew.kirillov@gmail.com
//

namespace AForge.Genetic
{
	using System;
	using System.Text;

	/// <summary>
	/// Permutation Chromosome is a Short Array Chromosome, but with two
	/// restrictions:
	/// 1) all genes are unique, i.e. there are no two genes in such
	///    chromosome with the same value;
	/// 2) maximum value of each gene is equal to chromosome length - 1.
	/// 
	/// With these two restrictions the chromosome represents permutation.
	/// </summary>
	public class PermutationChromosome : ShortArrayChromosome
	{
		/// <summary>
		/// Constructor
		/// </summary>
		public PermutationChromosome( int length ) : base( length, length - 1 ) { }

		/// <summary>
		/// Copy Constructor
		/// </summary>
		protected PermutationChromosome( PermutationChromosome source ) : base( source ) { }

		/// <summary>
		/// Generate random chromosome value
		/// </summary>
		public override void Generate( )
		{
			// create ascending permutation initially
			for ( int i = 0; i < length; i++ )
			{
				val[i] = (ushort) i;
			}

			// shufle the permutation
			for ( int i = 0, n = length >> 1; i < n; i++ )
			{
				ushort t;
				int j1 = rand.Next( length );
				int j2 = rand.Next( length );

				// swap values
				t		= val[j1];
				val[j1]	= val[j2];
				val[j2]	= t;
			}
		}

		/// <summary>
		/// Create new random chromosome (factory method)
		/// </summary>
		public override IChromosome CreateOffspring( )
		{
			return new PermutationChromosome( length );
		}

		/// <summary>
		/// Clone the chromosome
		/// </summary>
		public override IChromosome Clone( )
		{
			return new PermutationChromosome( this );
		}
		
		/// <summary>
		/// Mutation operator
		/// </summary>
		public override void Mutate( )
		{
			ushort t;
			int j1 = rand.Next( length );
			int j2 = rand.Next( length );

			// swap values
			t		= val[j1];
			val[j1]	= val[j2];
			val[j2]	= t;
		}

		/// <summary>
		/// Crossover operator
		/// </summary>
		public override void Crossover( IChromosome pair )
		{
			PermutationChromosome p = (PermutationChromosome) pair;

			// check for correct pair
			if ( ( p != null ) && ( p.length == length ) )
			{
				ushort[] child1 = new ushort[length];
				ushort[] child2 = new ushort[length];

				// create two children
				CreateChildUsingCrossover( this.val, p.val, child1 );
				CreateChildUsingCrossover( p.val, this.val, child2 );

				// replace parents with children
				this.val	= child1;
				p.val		= child2;
			}
		}

		// Produce new child applying crossover to two parents
		private void CreateChildUsingCrossover( ushort[] parent1, ushort[] parent2, ushort[] child )
		{
			// temporary array to specify if certain gene already
			// present in the child
			bool[]	geneIsBusy = new bool[length];
			// previous gene in the child and two next candidates
			ushort	prev, next1, next2;
			// candidates validness - candidate is valid, if it is not
			// yet in the child
			bool	valid1, valid2;

			int		j, k = length - 1;

			// first gene of the child is taken from the second parent
			prev = child[0] = parent2[0];
			geneIsBusy[prev] = true;

			// resolve all other genes of the child
			for ( int i = 1; i < length; i++ )
			{
				// find the next gene after PREV in both parents
				// 1
				for ( j = 0; j < k; j++ )
				{
					if ( parent1[j] == prev )
						break;
				}
				next1 = ( j == k ) ? parent1[0] : parent1[j + 1];
				// 2
				for ( j = 0; j < k; j++ )
				{
					if ( parent2[j] == prev )
						break;
				}
				next2 = ( j == k ) ? parent2[0] : parent2[j + 1];

				// check candidate genes for validness
				valid1 = !geneIsBusy[next1];
				valid2 = !geneIsBusy[next2];

				// select gene
				if ( valid1 && valid2 )
				{
					// both candidates are valid
					// select one of theme randomly
					prev = ( rand.Next( 2 ) == 0 ) ? next1 : next2;
				}
				else if ( !( valid1 || valid2 ) )
				{
					// none of candidates is valid, so
					// select random gene which is not in the child yet
					int r = j = rand.Next( length );

					// go down first
					while ( ( r < length ) && ( geneIsBusy[r] == true ) )
						r++;
					if ( r == length )
					{
						// not found, try to go up
						r = j - 1;
						while ( geneIsBusy[r] == true )	// && ( r >= 0 )
							r--;
					}
					prev = (ushort) r;
				}
				else
				{
					// one of candidates is valid
					prev = ( valid1 ) ? next1 : next2;
				}

				child[i] = prev;
				geneIsBusy[prev] = true;
			}
		}
	}
}

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This article, along with any associated source code and files, is licensed under The GNU General Public License (GPLv3)


Written By
Software Developer IBM
United Kingdom United Kingdom
Started software development at about 15 years old and it seems like now it lasts most part of my life. Fortunately did not spend too much time with Z80 and BK0010 and switched to 8086 and further. Similar with programming languages – luckily managed to get away from BASIC and Pascal to things like Assembler, C, C++ and then C#. Apart from daily programming for food, do it also for hobby, where mostly enjoy areas like Computer Vision, Robotics and AI. This led to some open source stuff like AForge.NET, Computer Vision Sandbox, cam2web, ANNT, etc.

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