基本信息
源码名称:C#遗传算法 示例源码
源码大小:0.10M
文件格式:.zip
开发语言:C#
更新时间:2016-11-13
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源码介绍
// All code copyright (c) 2003 Barry Lapthorn
// Website: http://www.lapthorn.net
//
// Disclaimer:
// All code is provided on an "AS IS" basis, without warranty. The author
// makes no representation, or warranty, either express or implied, with
// respect to the code, its quality, accuracy, or fitness for a specific
// purpose. Therefore, the author shall not have any liability to you or any
// other person or entity with respect to any liability, loss, or damage
// caused or alleged to have been caused directly or indirectly by the code
// provided. This includes, but is not limited to, interruption of service,
// loss of data, loss of profits, or consequential damages from the use of
// this code.
//
//
// $Author: barry $
// $Revision: 1.1 $
//
// $Id: Test.cs,v 1.1 2003/08/19 20:59:05 barry Exp $
using System;
using btl.generic;
public class Test
{
// optimal solution for this is (0.5,0.5)
public static double theActualFunction(double[] values)
{
if (values.GetLength(0) != 2)
throw new ArgumentOutOfRangeException("should only have 2 args");
double x = values[0];
double y = values[1];
double n = 9; // should be an int, but I don't want to waste time casting.
double f1 = Math.Pow(15*x*y*(1-x)*(1-y)*Math.Sin(n*Math.PI*x)*Math.Sin(n*Math.PI*y),2);
return f1;
}
public static void Main()
{
// Crossover = 80%
// Mutation = 5%
// Population size = 100
// Generations = 2000
// Genome size = 2
GA ga = new GA(0.8,0.05,100,2000,2);
ga.FitnessFunction = new GAFunction(theActualFunction);
//ga.FitnessFile = @"H:\fitness.csv";
ga.Elitism = true;
ga.Go();
double[] values;
double fitness;
ga.GetBest(out values, out fitness);
System.Console.WriteLine("Best ({0}):", fitness);
for (int i = 0 ; i < values.Length ; i )
System.Console.WriteLine("{0} ", values[i]);
ga.GetWorst(out values, out fitness);
System.Console.WriteLine("\nWorst ({0}):", fitness);
for (int i = 0 ; i < values.Length ; i )
System.Console.WriteLine("{0} ", values[i]);
}
}