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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) 2002-2021  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.bits.SinglePointCrossover;
import org.cicirello.search.representations.BitVector;

/**
 * This class is an implementation of the simple genetic algorithm (Simple 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. The crossover operator is single-point crossover (see the {@link
 * SinglePointCrossover} class), and the selection operator is fitness proportional (see the {@link
 * FitnessProportionalSelection} class).
 *
 * The library also includes other classes for evolutionary algorithms that may be more relevant
 * depending upon your use-case. For example, see the {@link GeneticAlgorithm} class for greater
 * flexibility in configuring the crossover and selection operators, 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 final class SimpleGeneticAlgorithm extends GeneticAlgorithm {

  /**
   * Initializes a simple genetic algorithm with a generational model where children replace the
   * parents, using the standard bit flip mutation, single-point crossover (the {@link
   * SinglePointCrossover} class), and fitness-proportional selection (the {@link
   * FitnessProportionalSelection} class). 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 crossoverRate The probability that a pair of parents undergo crossover.
   * @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 f, or tracker are null.
   */
  public SimpleGeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Double f,
      double mutationRate,
      double crossoverRate,
      ProgressTracker tracker) {
    super(
        n,
        bitLength,
        f,
        mutationRate,
        new SinglePointCrossover(),
        crossoverRate,
        new FitnessProportionalSelection(),
        tracker);
  }

  /**
   * Initializes a simple genetic algorithm with a generational model where children replace the
   * parents, using the standard bit flip mutation, single-point crossover (the {@link
   * SinglePointCrossover} class), and fitness-proportional selection (the {@link
   * FitnessProportionalSelection} class). 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 crossoverRate The probability that a pair of parents undergo crossover.
   * @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 f, or tracker are null.
   */
  public SimpleGeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Integer f,
      double mutationRate,
      double crossoverRate,
      ProgressTracker tracker) {
    super(
        n,
        bitLength,
        f,
        mutationRate,
        new SinglePointCrossover(),
        crossoverRate,
        new FitnessProportionalSelection(),
        tracker);
  }

  /**
   * Initializes a simple genetic algorithm with a generational model where children replace the
   * parents, using the standard bit flip mutation, single-point crossover (the {@link
   * SinglePointCrossover} class), and fitness-proportional selection (the {@link
   * FitnessProportionalSelection} class). 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 crossoverRate The probability that a pair of parents undergo crossover.
   * @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 f is null.
   */
  public SimpleGeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Double f,
      double mutationRate,
      double crossoverRate) {
    super(
        n,
        bitLength,
        f,
        mutationRate,
        new SinglePointCrossover(),
        crossoverRate,
        new FitnessProportionalSelection());
  }

  /**
   * Initializes a simple genetic algorithm with a generational model where children replace the
   * parents, using the standard bit flip mutation, single-point crossover (the {@link
   * SinglePointCrossover} class), and fitness-proportional selection (the {@link
   * FitnessProportionalSelection} class). 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 crossoverRate The probability that a pair of parents undergo crossover.
   * @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 f is null.
   */
  public SimpleGeneticAlgorithm(
      int n,
      int bitLength,
      FitnessFunction.Integer f,
      double mutationRate,
      double crossoverRate) {
    super(
        n,
        bitLength,
        f,
        mutationRate,
        new SinglePointCrossover(),
        crossoverRate,
        new FitnessProportionalSelection());
  }

  /*
   * Internal constructor for use by split method
   */
  private SimpleGeneticAlgorithm(SimpleGeneticAlgorithm 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 SimpleGeneticAlgorithm split() {
    return new SimpleGeneticAlgorithm(this);
  }
}