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Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.
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/*
 * Chips-n-Salsa: A library of parallel self-adaptive local search algorithms.
 * Copyright (C) 2002-2022 Vincent A. Cicirello
 *
 * This file is part of Chips-n-Salsa (https://chips-n-salsa.cicirello.org/).
 *
 * Chips-n-Salsa is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Chips-n-Salsa is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */

package org.cicirello.search.evo;

import org.cicirello.search.ProgressTracker;
import org.cicirello.search.operators.CrossoverOperator;
import org.cicirello.search.operators.Initializer;
import org.cicirello.search.operators.bits.BitFlipMutation;
import org.cicirello.search.operators.bits.BitVectorInitializer;
import org.cicirello.search.representations.BitVector;

/**
 * This class is an implementation of a genetic algorithm (GA) with the common bit vector
 * representation of solutions to optimization problems, and the generational model where children
 * replace their parents each generation. It uses the usual bit flip mutation, where each bit of
 * each member of the population is mutated (flipped) with some probability, known as the mutation
 * rate, each generation. All other genetic operators, such as crossover and selection are
 * configurable. This GeneticAlgorithm class can also be configured with or without the use of
 * elitism. With elitism, a specified number of the most fit members of the population survive into
 * the next generation unaltered.
 *
 * The library also includes other classes for evolutionary algorithms that may be more relevant
 * depending upon your use-case. For example, see the {@link SimpleGeneticAlgorithm} class for the
 * form of GA known as the Simple GA, the {@link MutationOnlyGeneticAlgorithm} class if all you want
 * to use is mutation and no crossover, and the {@link GenerationalEvolutionaryAlgorithm} class if
 * you want to optimize something other than BitVectors or if you want even greater flexibility in
 * configuring your evolutionary search.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public class GeneticAlgorithm extends GenerationalEvolutionaryAlgorithm {

  // Constructors with Initializer.

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type double, the {@link FitnessFunction.Double} interface.
   *
   * @param n The population size.
   * @param initializer An initializer for generating random initial population members.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param eliteCount The number of elite population members. Pass 0 for no elitism. eliteCount
   *     must be less than n.
   * @param tracker A ProgressTracker.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if eliteCount is greater than or equal to n.
   * @throws NullPointerException if any of crossover, initializer, f, selection, or tracker are
   *     null.
   */
  public GeneticAlgorithm(
      int n,
      Initializer initializer,
      FitnessFunction.Double f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      int eliteCount,
      ProgressTracker tracker) {
    super(
        n,
        new BitFlipMutation(mutationRate),
        1.0,
        crossover,
        crossoverRate,
        initializer,
        f,
        selection,
        eliteCount,
        tracker);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type int, the {@link FitnessFunction.Integer} interface.
   *
   * @param n The population size.
   * @param initializer An initializer for generating random initial population members.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param eliteCount The number of elite population members. Pass 0 for no elitism. eliteCount
   *     must be less than n.
   * @param tracker A ProgressTracker.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if eliteCount is greater than or equal to n.
   * @throws NullPointerException if any of crossover, initializer, f, selection, or tracker are
   *     null.
   */
  public GeneticAlgorithm(
      int n,
      Initializer initializer,
      FitnessFunction.Integer f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      int eliteCount,
      ProgressTracker tracker) {
    super(
        n,
        new BitFlipMutation(mutationRate),
        1.0,
        crossover,
        crossoverRate,
        initializer,
        f,
        selection,
        eliteCount,
        tracker);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type double, the {@link FitnessFunction.Double} interface.
   *
   * @param n The population size.
   * @param initializer An initializer for generating random initial population members.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param tracker A ProgressTracker.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws NullPointerException if any of crossover, initializer, f, selection, or tracker are
   *     null.
   */
  public GeneticAlgorithm(
      int n,
      Initializer initializer,
      FitnessFunction.Double f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      ProgressTracker tracker) {
    this(n, initializer, f, mutationRate, crossover, crossoverRate, selection, 0, tracker);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type int, the {@link FitnessFunction.Integer} interface.
   *
   * @param n The population size.
   * @param initializer An initializer for generating random initial population members.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param tracker A ProgressTracker.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws NullPointerException if any of crossover, initializer, f, selection, or tracker are
   *     null.
   */
  public GeneticAlgorithm(
      int n,
      Initializer initializer,
      FitnessFunction.Integer f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      ProgressTracker tracker) {
    this(n, initializer, f, mutationRate, crossover, crossoverRate, selection, 0, tracker);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type double, the {@link FitnessFunction.Double} interface.
   *
   * @param n The population size.
   * @param initializer An initializer for generating random initial population members.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param eliteCount The number of elite population members. Pass 0 for no elitism. eliteCount
   *     must be less than n.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if eliteCount is greater than or equal to n.
   * @throws NullPointerException if any of crossover, initializer, f, or selection are null.
   */
  public GeneticAlgorithm(
      int n,
      Initializer initializer,
      FitnessFunction.Double f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      int eliteCount) {
    super(
        n,
        new BitFlipMutation(mutationRate),
        1.0,
        crossover,
        crossoverRate,
        initializer,
        f,
        selection,
        eliteCount);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type int, the {@link FitnessFunction.Integer} interface.
   *
   * @param n The population size.
   * @param initializer An initializer for generating random initial population members.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param eliteCount The number of elite population members. Pass 0 for no elitism. eliteCount
   *     must be less than n.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if eliteCount is greater than or equal to n.
   * @throws NullPointerException if any of crossover, initializer, f, or selection are null.
   */
  public GeneticAlgorithm(
      int n,
      Initializer initializer,
      FitnessFunction.Integer f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      int eliteCount) {
    super(
        n,
        new BitFlipMutation(mutationRate),
        1.0,
        crossover,
        crossoverRate,
        initializer,
        f,
        selection,
        eliteCount);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type double, the {@link FitnessFunction.Double} interface.
   *
   * @param n The population size.
   * @param initializer An initializer for generating random initial population members.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws NullPointerException if any of crossover, initializer, f, or selection are null.
   */
  public GeneticAlgorithm(
      int n,
      Initializer initializer,
      FitnessFunction.Double f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection) {
    this(n, initializer, f, mutationRate, crossover, crossoverRate, selection, 0);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type int, the {@link FitnessFunction.Integer} interface.
   *
   * @param n The population size.
   * @param initializer An initializer for generating random initial population members.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws NullPointerException if any of crossover, initializer, f, or selection are null.
   */
  public GeneticAlgorithm(
      int n,
      Initializer initializer,
      FitnessFunction.Integer f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection) {
    this(n, initializer, f, mutationRate, crossover, crossoverRate, selection, 0);
  }

  // Constructors specifying length of bit vectors instead of Initializer object.

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type double, the {@link FitnessFunction.Double} interface.
   *
   * @param n The population size.
   * @param bitLength The length of each bit vector.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param eliteCount The number of elite population members. Pass 0 for no elitism. eliteCount
   *     must be less than n.
   * @param tracker A ProgressTracker.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if bitLength is negative
   * @throws IllegalArgumentException if eliteCount is greater than or equal to n.
   * @throws NullPointerException if any of crossover, f, selection, or tracker are null.
   */
  public GeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Double f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      int eliteCount,
      ProgressTracker tracker) {
    this(
        n,
        new BitVectorInitializer(bitLength),
        f,
        mutationRate,
        crossover,
        crossoverRate,
        selection,
        eliteCount,
        tracker);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type int, the {@link FitnessFunction.Integer} interface.
   *
   * @param n The population size.
   * @param bitLength The length of each bit vector.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param eliteCount The number of elite population members. Pass 0 for no elitism. eliteCount
   *     must be less than n.
   * @param tracker A ProgressTracker.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if bitLength is negative
   * @throws IllegalArgumentException if eliteCount is greater than or equal to n.
   * @throws NullPointerException if any of crossover, f, selection, or tracker are null.
   */
  public GeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Integer f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      int eliteCount,
      ProgressTracker tracker) {
    this(
        n,
        new BitVectorInitializer(bitLength),
        f,
        mutationRate,
        crossover,
        crossoverRate,
        selection,
        eliteCount,
        tracker);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type double, the {@link FitnessFunction.Double} interface.
   *
   * @param n The population size.
   * @param bitLength The length of each bit vector.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param tracker A ProgressTracker.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if bitLength is negative
   * @throws NullPointerException if any of crossover, f, selection, or tracker are null.
   */
  public GeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Double f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      ProgressTracker tracker) {
    this(n, bitLength, f, mutationRate, crossover, crossoverRate, selection, 0, tracker);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type int, the {@link FitnessFunction.Integer} interface.
   *
   * @param n The population size.
   * @param bitLength The length of each bit vector.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param tracker A ProgressTracker.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if bitLength is negative.
   * @throws NullPointerException if any of crossover, f, selection, or tracker are null.
   */
  public GeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Integer f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      ProgressTracker tracker) {
    this(n, bitLength, f, mutationRate, crossover, crossoverRate, selection, 0, tracker);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type double, the {@link FitnessFunction.Double} interface.
   *
   * @param n The population size.
   * @param bitLength The length of each bit vector.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param eliteCount The number of elite population members. Pass 0 for no elitism. eliteCount
   *     must be less than n.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if bitLength is negative.
   * @throws IllegalArgumentException if eliteCount is greater than or equal to n.
   * @throws NullPointerException if any of crossover, f, or selection are null.
   */
  public GeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Double f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      int eliteCount) {
    this(
        n,
        new BitVectorInitializer(bitLength),
        f,
        mutationRate,
        crossover,
        crossoverRate,
        selection,
        eliteCount);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type int, the {@link FitnessFunction.Integer} interface.
   *
   * @param n The population size.
   * @param bitLength The length of each bit vector.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @param eliteCount The number of elite population members. Pass 0 for no elitism. eliteCount
   *     must be less than n.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if bitLength is negative.
   * @throws IllegalArgumentException if eliteCount is greater than or equal to n.
   * @throws NullPointerException if any of crossover, f, or selection are null.
   */
  public GeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Integer f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection,
      int eliteCount) {
    this(
        n,
        new BitVectorInitializer(bitLength),
        f,
        mutationRate,
        crossover,
        crossoverRate,
        selection,
        eliteCount);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type double, the {@link FitnessFunction.Double} interface.
   *
   * @param n The population size.
   * @param bitLength The length of each bit vector.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if bitLength is negative
   * @throws NullPointerException if any of crossover, f, or selection are null.
   */
  public GeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Double f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection) {
    this(n, bitLength, f, mutationRate, crossover, crossoverRate, selection, 0);
  }

  /**
   * Initializes a genetic algorithm with a generational model where children replace the parents,
   * using the standard bit flip mutation. All other characteristics, such as crossover operator and
   * selection operator are configurable. This constructor supports fitness functions with fitnesses
   * of type int, the {@link FitnessFunction.Integer} interface.
   *
   * @param n The population size.
   * @param bitLength The length of each bit vector.
   * @param f The fitness function.
   * @param mutationRate The per-bit probability of flipping a bit. Each bit of each member of the
   *     population is flipped with this probability, and the decisions to flip bits are
   *     independent.
   * @param crossover The crossover operator.
   * @param crossoverRate The probability that a pair of parents undergo crossover.
   * @param selection The selection operator.
   * @throws IllegalArgumentException if n is less than 1.
   * @throws IllegalArgumentException if mutationRate ≤ 0 or if mutationRate ≥ 1.
   * @throws IllegalArgumentException if crossoverRate is less than 0.
   * @throws IllegalArgumentException if bitLength is negative.
   * @throws NullPointerException if any of crossover, f, or selection are null.
   */
  public GeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Integer f,
      double mutationRate,
      CrossoverOperator crossover,
      double crossoverRate,
      SelectionOperator selection) {
    this(n, bitLength, f, mutationRate, crossover, crossoverRate, selection, 0);
  }

  /*
   * Internal constructor for use by split method
   * package private so subclasses in same package can use it for initialization for their own split methods.
   */
  GeneticAlgorithm(GeneticAlgorithm other) {
    super(other);
    // Just call super constructor to perform split() logic. This
    // subclass doesn't currently maintain any additional state.
    // Only reason for overriding split() method, and thus providing this
    // constructor is to ensure runtime type of split instance is same,
    // although strictly speaking it would still function correctly otherwise.
  }

  @Override
  public GeneticAlgorithm split() {
    return new GeneticAlgorithm(this);
  }
}