public GeneticAlgorithmFloat(List <DataFP> dataList, int numberOfRules, int totalGenerations,
float crossoverRate, int tournamentSize, float mutationRange, float mutationRate, int?populationSize = null)
{
// Condition length inferred from supplied data.
this.boundryLength = dataList[0].cond.Length;
// Population defaults to the size of the supplied dataList.
this.populationSize = populationSize ?? dataList.Count;
this.numberOfRules = numberOfRules;
this.totalGenerations = totalGenerations;
this.crossoverRate = crossoverRate;
this.mutationRate = mutationRate;
this.mutationRange = mutationRange;
this.mutationRateOriginal = mutationRate;
this.mutationRangeOriginal = mutationRange;
this.tournamentSize = tournamentSize;
this.tournamentSizeOriginal = tournamentSize;
// Split up our data into training and evaluation sets.
IndividualFP.SetTrainingAndEvaluationData(dataList.Take(1000).ToList(),
dataList.Skip(1000).Take(1000).ToList());
generationCounter = 1;
population = new List <IndividualFP>();
fitnessLookup = new Dictionary <IndividualFP, int>();
}
