public void InitGA()
{
System.Random rand;
rand = (seedRandom) ? new System.Random(1) : new System.Random();
var randGenomGenerator = new RandNeuralGenomeGeneratorBase(
rand,
new Sigmoid(),
weightRange,
inputCount,
outputCount,
hiddenLayers,
createBias);
var reinsertion = new ReinsertBest <Synapse>(
(int)(poplLne * reinsertPart));
var breeding = NewBreeding(rand);
var generationGenerator = new GenerationGeneratorBase <Synapse>(
poplLne,
reinsertion,
breeding);
population = new PopulationBase <Synapse>(
poplLne,
randGenomGenerator,
generationGenerator);
}
public GAEvolveSum(
Random rand,
double partToReinsert,
int targetSum,
int poplLen,
int genomeNumbers,
int nbInterval,
double geneMutChance,
int geneMutRange)
{
ES_RandGenomeGenerator randGenomeGenerator;
GenerationGeneratorBase <int> generationGenerator;
IReinsertion <int> reinsertion;
IBreeding <int> breeding;
RandomInst = rand;
TargetSum = targetSum;
FitnessEval = new ES_FitnessEval(targetSum);
randGenomeGenerator = new ES_RandGenomeGenerator(genomeNumbers,
nbInterval,
rand);
reinsertion = new ReinsertBest <int>(
(int)(poplLen * partToReinsert));
breeding = NewBreeding(
poplLen,
partToReinsert,
geneMutChance,
geneMutRange);
generationGenerator = new GenerationGeneratorBase <int>(
poplLen,
reinsertion,
breeding);
Population = new PopulationBase <int>(
poplLen,
randGenomeGenerator,
generationGenerator);
}
public ShakespeareSolver(
string targetStr,
Random rand,
double partToReinsert,
int popLen,
double geneMutChance,
int geneMutRange)
{
TargetStr = targetStr;
FitnessEval = new Sh_FitnessEval(TargetStr, chars);
var randGenomeGenerator = new Sh_RandGenomeGenerator(
rand,
targetStr.Length,
geneMutRange,
chars);
var reinsertion = new ReinsertBest <char>(
(int)(popLen * partToReinsert));
var breeding = NewBreeding(
rand,
popLen,
partToReinsert,
geneMutChance,
geneMutRange);
var generationGenerator = new GenerationGeneratorBase <char>(
popLen,
reinsertion,
breeding);
Population = new PopulationBase <char>(
popLen,
randGenomeGenerator,
generationGenerator);
}
public void InitGAInternal()
{
var randomGenomeGenerator = new RandNeuralGenomeGeneratorBase(
rand,
new Sigmoid(),
randomWeight,
inputCount,
outputCount,
hiddenLayers,
createBias);
var reinsertion = new ReinsertBest <Synapse>(
(int)(poplLen * partToReinsert));
var breeding = NewBreeding(rand);
var generationGenerator = new GenerationGeneratorBase <Synapse>(
poplLen,
reinsertion,
breeding);
population = new PopulationBase <Synapse>(
poplLen,
randomGenomeGenerator,
generationGenerator);
}
