Class GeneticAlgorithm<Entity>

constructor

• new GeneticAlgorithm<Entity extends WithFitness>(
      options: geneticAlgorithmOptions<Entity>,
  ): GeneticAlgorithm<Entity>

  Initializes a new instance of the Genetic Algorithm. *

  Type Parameters

  • Entity extends WithFitness

    The type of object representing an individual. Must extend WithFitness.

  Parameters

  • options: geneticAlgorithmOptions<Entity>

    Initial configuration object implementing geneticAlgorithmOptions.

  Returns GeneticAlgorithm<Entity>

  Throws

  If the initial population contains 1 or fewer individuals.

  Throws

  If the maximum population size (maxPopulationSize) is less than or equal to 1.

generation

population

evolve

• evolve(
      generations: number,
      callback?: (
          generation: number,
          population: Entity[],
          fittest: Entity,
      ) => void | Promise<void>,
  ): Promise<Entity>

  Starts or continues the evolution process for a given number of generations. The process will stop early if the fitnessObjective is met or if stop() is manually invoked. *

  Parameters

  • generations: number

    The maximum number of generations/cycles to run.

  • Optionalcallback: (
        generation: number,
        population: Entity[],
        fittest: Entity,
    ) => void | Promise<void>

    Optional lifecycle callback triggered after each generation completes.

  Returns Promise<Entity>

  A promise that resolves with the fittest individual found in the final population.

  Throws

  If the population becomes empty or corrupted at any point during evolution.

reset

• reset(): void

  Stops any active execution and fully resets the state of the algorithm (generation back to 1 and population reset to the original initial population).

  Returns void

setCrossoverMethod

• setCrossoverMethod(
      crossoverMethod: crossoverMethod<Entity>,
  ): GeneticAlgorithm<Entity>

  Sets the crossover method to mix the genetic code of the parents. *

  Parameters

  • crossoverMethod: crossoverMethod<Entity>

    Function that takes two parents and returns a new child combining their properties.

  Returns GeneticAlgorithm<Entity>

  The GeneticAlgorithm instance to enable method chaining (fluent API).

setFitnessFunction

• setFitnessFunction(
      fitnessFunction: fitnessFunction<Entity>,
  ): GeneticAlgorithm<Entity>

  Sets the fitness function to evaluate each individual in the population. *

  Parameters

  • fitnessFunction: fitnessFunction<Entity>

    Asynchronous function that calculates and returns the numerical fitness value.

  Returns GeneticAlgorithm<Entity>

  The GeneticAlgorithm instance to enable method chaining (fluent API).

setMutationMethod

• setMutationMethod(
      mutationMethod: mutationMethod<Entity>,
  ): GeneticAlgorithm<Entity>

  Sets the mutation method for the created children. *

  Parameters

  • mutationMethod: mutationMethod<Entity>

    Function that randomly alters features of a child individual.

  Returns GeneticAlgorithm<Entity>

  The GeneticAlgorithm instance to enable method chaining (fluent API).

setSelectionMethod

• setSelectionMethod(
      selectionMethod: selectionMethod<Entity>,
  ): GeneticAlgorithm<Entity>

  Sets the parent selection method. *

  Parameters

  • selectionMethod: selectionMethod<Entity>

    Function responsible for choosing two parents from the current population.

  Returns GeneticAlgorithm<Entity>

  The GeneticAlgorithm instance to enable method chaining (fluent API).

stop

• stop(): void

  Immediately stops the ongoing asynchronous evolution process (evolve) upon completing the current step.

  Returns void