static void Main(string[] args)
{
var problem = Problem.Read("Fitness\\sin-data.txt");
var code =
"((((0 = (0 - ((4 <= X0) / 9.5))) OR ((1.5 XOR 0) - (X0 AND 1.5))) XOR (((2 OR 7.1) / Y0)=>(Y0 / (Y1 = Y1)))) - ((((X0 + X0) = (Y1=>0)) XOR (Y0 = (3 >= X0))) > Y0))";
var program = Parser.Parser.Parse(Tokenizer.Tokenizer.Tokenize(code));
var fitness = new FitnessEvaluation(program, problem);
var stringBuilder = new StringBuilder();
fitness.Evaluate(stringBuilder);
FitnessEvaluation simplifiedFitness;
do
{
gp.Program simplifiedProgram = program.Simplify(fitness, stringBuilder);
if (simplifiedProgram == null || simplifiedProgram.ToString() == program.ToString())
{
break;
}
simplifiedFitness = new FitnessEvaluation(simplifiedProgram, problem);
simplifiedFitness.Evaluate(false, stringBuilder);
program = simplifiedProgram;
} while (fitness.ErrorSum == simplifiedFitness.ErrorSum);
Console.Out.Write(stringBuilder);
}
public Program()
{
GARandomManager.Random = new RandomClassic((int)DateTime.Now.Ticks);
var generationManager = new GenerationManagerKeepLast();
var initialGenerationGenerator = new GASumInitialGenerationGenerator
{
GeneCount = genesCount,
MinValue = minGeneValue,
MaxValue = maxGeneValue
};
var selection = new EliteSelection();
var crossover = new OnePointCrossover(useBothChildren: true);
var breeding = new BreedingClassic(
crossoverPart,
minProduction: 1,
selection: selection,
crossover: crossover,
mutationManager: InitMutations());
var reinsertion = new ReinsertionFromSelection(
reinsertionPart,
minProduction: 1,
selection: new EliteSelection());
var producers = new List <IGenomeProducer>
{
reinsertion,
breeding
};
var genomeForge = new GenomeForge(producers);
geneticManager = new GeneticManagerClassic(
generationManager,
initialGenerationGenerator,
genomeForge,
genomesCount
);
geneticManager.Init();
fitnessEvaluation = new FitnessEvaluation();
}
