遗传算法是通过模拟生物进化而进行数据寻优的一种进化算法。主要过程是初始种群的产生，选择，交叉，变异，循环迭代，直至出现最优解。本程序两个主要类，种群类与个体类。定义进化策略接口，计算适应度策略接口。进化策略接口实现三个行为，交叉，变异，选择，其中，进化策略接口可以加上自己的实现。大致实现如下：
//
//class Population
//
using System;
using System.Collections.Generic;
using System.Collections;
using System.Text;
namespace SGA.SingleGA
{
/*
群体类，实现了种群初始化，有交叉，变异行为，选择等行为。同时统计种群的平均适应度，最大适应度，适应度之和。
*/
public class Population : CollectionBase,ICloneable,IDisposable
{
private IGeneticStrategy _iGeneticStrategy;
public Population(IGeneticStrategy iGeneticStrategy) { this._iGeneticStrategy = iGeneticStrategy; }
public Population() { }
public void Init(int iMax)
{
Random rd = new Random();
for (int i = 0; i < iMax; i++)
{
StringBuilder sb = new StringBuilder();
for (int j = 0; j < 22; j++)
{
sb.Append(rd.Next(0,2));
}
this.List.Add(new Individual(sb.ToString()));
}
}
public double MaxFitness
{
get
{
double dmax = double.MinValue;
for (int i = 0; i < List.Count; i++)
{
if ((List[i] as Individual).Fitness > dmax) dmax = (List[i] as Individual).Fitness;
}
return dmax;
}
}
public double AverageFitness
{
get
{
return SumFitness / List.Count;
}
}
public double SumFitness
{
get
{
double dSum = 0;
for (int i = 0; i < List.Count; i++)
{
dSum += (List[i] as Individual).Fitness;
}
return dSum;
}
}
public IGeneticStrategy GeneticStrategy
{
get { return this._iGeneticStrategy; }
set { this._iGeneticStrategy = value; }
}
public Population Select()
{
if (_iGeneticStrategy == null) return null;
return _iGeneticStrategy.Select(this);
}
public void Crossover()
{
if (_iGeneticStrategy == null) return;
_iGeneticStrategy.Crossover(this);
}
public void Mutation()
{
if (_iGeneticStrategy == null) return;
_iGeneticStrategy.Mutation(this);
}
public Individual this[int index]
{
get { return (Individual)this.List[index]; }
set { this.List[index] = value; }
}
public void Add(Individual ind)
{
this.List.Add(ind);
}
public int Indexof(Individual ind)
{
return this.List.IndexOf(ind);
}
public void Print()
{
for (int i = 0; i < List.Count; i++)
{
Console.WriteLine(“第{0}个体fit={2} {1}”, i.ToString(), (List[i] as Individual).Variable.ToString(), (List[i] as Individual).Fitness);
}
}
#region ICloneable 成员
public object Clone()
{
Population pop = new Population(this.GeneticStrategy);
for (int i = 0; i < this.List.Count; i++)
pop.List.Add(this.List[i]);
return pop;
}
#endregion
#region IDisposable 成员
public void Dispose()
{
_iGeneticStrategy = null;
}
#endregion
}
}
//
//Individual
//
using System;
using System.Collections.Generic;
using System.Text;
namespace SGA.SingleGA
{
public class Individual : ICloneable
{
private string _gene;
private double _fitness = double.MinValue;
public static IFitness _calFit = new OneDFitness();
public string Gene
{
get { return _gene; }
set
{
_gene = value;
_fitness = _calFit.Fitness(Variable);
}
}
public double Fitness
{
get { return _fitness; }
}
public double Variable
{
get { return Coder.ToReal(_gene, -1.0, 2.0); }
}
public Individual()
{
}
public Individual(string sGene)
{
Gene = sGene;
}
public Individual(string sGene,IFitness calFit)
{
_calFit = calFit;
this.Gene = sGene;
}
//public IFitness CalFit
//{
// get { return this._calFit; }
// set
// {
// this._calFit = value;
// _fitness = _calFit.Fitness(Coder.ToReal(_gene, -1.0, 2.0));
// }
//}
public int ToMetrication()
{
return Convert.ToInt32(Gene, 2);
}
public static int operator * (Individual ind1,Individual ind2)
{
Random rd = new Random();
int iStart = rd.Next(0, ind1.Gene.Length-2);
int iLast = rd.Next(iStart+1,ind1.Gene.Length-1);
while (ind1.Gene.Substring(iStart, iLast – iStart) == ind2.Gene.Substring(iStart, iLast – iStart))
{
iStart = rd.Next(0, ind1.Gene.Length – 2);
iLast = rd.Next(iStart + 1, ind1.Gene.Length – 1);
}
StringBuilder sbGene1 = new StringBuilder();
sbGene1.Append(ind1.Gene.Substring(0, iStart));
sbGene1.Append(ind2.Gene.Substring(iStart, iLast-iStart));
sbGene1.Append(ind1.Gene.Substring(iLast));
StringBuilder sbGene2 = new StringBuilder();
sbGene2.Append(ind2.Gene.Substring(0, iStart));
sbGene2.Append(ind1.Gene.Substring(iStart,iLast-iStart));
sbGene2.Append(ind2.Gene.Substring(iLast));
ind1.Gene = sbGene1.ToString();
ind2.Gene = sbGene2.ToString();
return iLast – iStart;
}
public int Crossover(Individual ind)
{
return this * ind;
}
public int Mutation()
{
Random rd = new Random();
int iPos = rd.Next(0, this.Gene.Length-1);
StringBuilder sb = new StringBuilder(this.Gene);
sb[iPos] = sb[iPos] == ‘0’ ? ‘1’ : ‘0’;
this.Gene = sb.ToString();
return iPos;
}
public override string ToString()
{
return this.Gene;
}
public override bool Equals(object obj)
{
return base.Equals(obj);
}
public override int GetHashCode()
{
return base.GetHashCode();
}
#region ICloneable 成员
public object Clone()
{
return new Individual(this.Gene);
}
#endregion
}
}
