longbiscuit
commented
3 years ago i also can't get better fittness,why: `using GeneticSharp.Domain; using GeneticSharp.Domain.Chromosomes; using GeneticSharp.Domain.Crossovers; using GeneticSharp.Domain.Fitnesses; using GeneticSharp.Domain.Mutations; using GeneticSharp.Domain.Populations; using GeneticSharp.Domain.Selections; using GeneticSharp.Domain.Terminations; using GeneticSharp.Infrastructure.Framework.Threading; using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Threading.Tasks; //https://diegogiacomelli.com.br/function-optimization-with-geneticsharp/ //多线程 https://github.com/giacomelli/GeneticSharp/wiki/multithreading namespace ConsoleApp1GeneticSharp { class Program {
static void Main(string[] args)
{
#region example 1
// https://www.nuget.org/packages/GeneticSharp/
//https://stackoverflow.com/questions/59191025/optimization-of-an-equation-parameters-using-genetic-sharp-genetic-algorithm
//x = 1, 2, 3, 4, 5
//y = 5, 4, 3, 2, 1
//z = 1, 2, 1, 1, 3
//t = 9, 12, 9, 9, 15.
// t=ax+by+c*z. here i know a=1,b=1 and c=3
double[] x = new double[] { 1, 2, 3, 4, 5 };
double[] y = new double[] { 5, 4, 3, 2, 1 };
double[] z = new double[] { 1, 2, 1, 1, 3 };
double[] t = new double[] { 9, 12, 9, 9, 15 };
var chromosome = new FloatingPointChromosome(
new double[] { 0.0, 0.0, 0.0 },//各参数最小值
new double[] { 10.0, 10.0, 10.0 },//各参数最大值
//new double[] { 1, 1, 3 },//各参数最小值
//new double[] { 1, 1, 3 },//各参数最大值
new int[] { 64, 64, 64 },//用于表示上述参数值的2进制位数
new int[] { 3, 3, 3 });//参数的十进制形式小数位数
//https://github.com/giacomelli/GeneticSharp/issues/4
var population = new Population(100, 100, chromosome);//select the % of individuals who will survive in each generation
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
double err = 0;
var values = fc.ToFloatingPoints();
var pa = values[0];
var pb = values[1];
var pc = values[2];
for (int i = 0; i < x.Count(); i++)
{
err -= Math.Pow((t[i] - (pa * x[i] + pb * y[i] + pc * z[i])), 2);
}
return err;
});
//给定三大算子
var selection = new TournamentSelection();// Elite, Roulete Wheel, Stochastic Universal Sampling and Tournament. //https://diegogiacomelli.com.br/function-optimization-with-geneticsharp/
var crossover = new TwoPointCrossover();//https://github.com/giacomelli/GeneticSharp/wiki/crossovers
var mutation = new FlipBitMutation();//https://github.com/giacomelli/GeneticSharp/wiki/mutations
//多线程
var taskExecutor = new ParallelTaskExecutor();//多线程
taskExecutor.MinThreads = 2;//多线程
taskExecutor.MaxThreads = 2;//多线程
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.TaskExecutor = taskExecutor;//多线程
//var termination = new FitnessStagnationTermination(1000);
//ga.Termination = termination;
//ga.Termination = new GenerationNumberTermination(3000);
ga.Termination = new OrTermination(
new GenerationNumberTermination(5000), //执行到多少代必须停止
new FitnessStagnationTermination(2000));//目标函数多少代不再变化就停止
ga.MutationProbability = 0.05f;
ga.CrossoverProbability = 0.85f;
Console.WriteLine("GA running...");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.ToFloatingPoints();
Console.WriteLine(
"Generation {0}: {1},{2},{3} = {4}",
ga.GenerationsNumber,
phenotype[0],
phenotype[1],
phenotype[2],
bestFitness
);
}
};
ga.Start();
Console.WriteLine("GA ending...");
Console.ReadKey();
#endregion example 1
//#region example 2
//float maxWidth = 998f;
//float maxHeight = 680f;
//var chromosome = new FloatingPointChromosome(
// new double[] { 0, 0, 0, 0 },
// new double[] { maxWidth, maxHeight, maxWidth, maxHeight },
// new int[] { 10, 10, 10, 10 },
// new int[] { 0, 0, 0, 0 });
//var population = new Population(100, 100, chromosome);
//var fitness = new FuncFitness((c) =>
//{
// var fc = c as FloatingPointChromosome;
// var values = fc.ToFloatingPoints();
// var x1 = values[0];
// var y1 = values[1];
// var x2 = values[2];
// var y2 = values[3];
// return Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2));
//});
//var selection = new EliteSelection();
//var crossover = new UniformCrossover(0.5f);
//var mutation = new FlipBitMutation();
//var termination = new FitnessStagnationTermination(100);
//var ga = new GeneticAlgorithm(
// population,
// fitness,
// selection,
// crossover,
// mutation);
//ga.Termination = termination;
//Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
//var latestFitness = 0.0;
//ga.GenerationRan += (sender, e) =>
//{
// var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
// var bestFitness = bestChromosome.Fitness.Value;
// if (bestFitness != latestFitness)
// {
// latestFitness = bestFitness;
// var phenotype = bestChromosome.ToFloatingPoints();
// Console.WriteLine(
// "Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
// ga.GenerationsNumber,
// phenotype[0],
// phenotype[1],
// phenotype[2],
// phenotype[3],
// bestFitness
// );
// }
//};
//ga.Start();
//Console.ReadKey();
//#endregion example 2
}
}} `
have a problem figuring out how to set my function. It should be pretty easy but i am new to GA and espectially to GeneticSharp. So i need to set and optimize this function: -(16 x x - 24 x + 5) Math.Exp(-x) with scope from 1.9 to 3.9, thats all. But right now it runs only once and i think i did something wrong. Thank you in advance! `namespace ConsoleApp1 { class MainClass { public static void Main(string[] args) {
}`