GeneticSettings

public Type Crossover { get; set; }

value = geneticSettings.Crossover
geneticSettings.Crossover = value

ICrossover

public decimal CrossoverProbability { get; set; }

value = geneticSettings.CrossoverProbability
geneticSettings.CrossoverProbability = value

CrossoverProbability

public string Fitness { get; set; }

value = geneticSettings.Fitness
geneticSettings.Fitness = value

Fitness function formula. For example, 'PnL'.

public int GenerationsMax { get; set; }

value = geneticSettings.GenerationsMax
geneticSettings.GenerationsMax = value

Maximum number of generations.

public int GenerationsStagnation { get; set; }

value = geneticSettings.GenerationsStagnation
geneticSettings.GenerationsStagnation = value

The genetic algorithm will be terminate when the best chromosome's fitness has no change in the last generations specified.

public Type Mutation { get; set; }

value = geneticSettings.Mutation
geneticSettings.Mutation = value

IMutation

public decimal MutationProbability { get; set; }

value = geneticSettings.MutationProbability
geneticSettings.MutationProbability = value

MutationProbability

public int Population { get; set; }

value = geneticSettings.Population
geneticSettings.Population = value

The initial size of population.

public int PopulationMax { get; set; }

value = geneticSettings.PopulationMax
geneticSettings.PopulationMax = value

The maximum population.

public Type Reinsertion { get; set; }

value = geneticSettings.Reinsertion
geneticSettings.Reinsertion = value

IReinsertion

public Type Selection { get; set; }

value = geneticSettings.Selection
geneticSettings.Selection = value

ISelection