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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.operators.integers;

import org.cicirello.search.operators.UndoableMutationOperator;
import org.cicirello.search.representations.IntegerValued;

/**
 * This mutation operator (supporting the undo operation) is for integer valued representations, and
 * replaces an integer value with a different random integer value from the domain. The domain is
 * specified with an interval: [a, b]. The parameter p specifies the probability of mutating an
 * integer. E.g., if the {@link IntegerValued} object undergoing mutation has n integers, then on
 * average the {@link #mutate} method will mutate k=n*p of those integers. The k integers chosen for
 * mutation are chosen uniformly at random. For each of those k integers, the new value is chosen
 * uniformly at random from [a, b] but excluding its current value. For example, let [a, b]=[0,4],
 * and consider mutating an integer v whose value is currently v=3. The new value for v will be
 * chosen uniformly at random from the set {0, 1, 2, 4}. Note that when a=0 and b=1, this mutation
 * operator becomes equivalent to the traditional bit-flip mutation commonly used in genetic
 * algorithms when solutions are represented as bit strings, although use of this class and the
 * {@link IntegerValued} class for that purpose is not recommended as there are much more efficient
 * ways of representing strings of bits (e.g., using bit level operators).
 *
 * @param  The specific IntegerValued type.
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class UndoableRandomValueChangeMutation
    extends RandomValueChangeMutation implements UndoableMutationOperator {

  private int[] oldA;

  /**
   * Constructs a UndoableRandomValueChangeMutation operator that always mutates exactly one integer
   * from the IntegerValued. If the IntegerValued is a univariate, then it mutates the one and only
   * one integer. If it is a multivariate, then one integer parameter is chosen for mutation
   * uniformly at random.
   *
   * @param a The lower bound of the domain from which to choose random values.
   * @param b The upper bound of the domain from which to choose random values. b must be greater
   *     than a (i.e., there must be at least 2 values in the domain).
   * @throws IllegalArgumentException if a ≥ b.
   */
  public UndoableRandomValueChangeMutation(int a, int b) {
    super(a, b, 0.0, 1);
  }

  /**
   * Constructs a UndoableRandomValueChangeMutation operator. If the IntegerValued undergoing
   * mutation contains n integer parameters, then this mutation operator will mutate n*p of those
   * integers on average during calls to {@link #mutate}. Since this is a randomized process, it is
   * possible that no integers will be mutated during a call to mutate (e.g., if p is low relative
   * to n).
   *
   * @param a The lower bound of the domain from which to choose random values.
   * @param b The upper bound of the domain from which to choose random values. b must be greater
   *     than a (i.e., there must be at least 2 values in the domain).
   * @param p The probability of mutating an individual integer. Negative p are treated as p=0. If p
   *     is greater than 1, it is treated as p=1.
   */
  public UndoableRandomValueChangeMutation(int a, int b, double p) {
    super(a, b, p, 0);
  }

  /*
   * internal copy constructor to support split method
   */
  UndoableRandomValueChangeMutation(UndoableRandomValueChangeMutation other) {
    super(other);
  }

  /**
   * Constructs a UndoableRandomValueChangeMutation operator. If the IntegerValued undergoing
   * mutation contains n integer parameters, then this mutation operator will mutate n*p of those
   * integers on average during calls to {@link #mutate}, but will definitely mutate at least k of
   * them. Use this constructor if you want to insure that every call to {@link #mutate} changes the
   * IntegerValued undergoing mutation by specifying a minimum k to mutate.
   *
   * @param a The lower bound of the domain from which to choose random values.
   * @param b The upper bound of the domain from which to choose random values. b must be greater
   *     than a (i.e., there must be at least 2 values in the domain).
   * @param p The probability of mutating an individual integer. Negative p are treated as p=0. If p
   *     is greater than 1, it is treated as p=1.
   * @param k The minimum number of integer parameters of the IntegerValued undergoing mutation to
   *     mutate during calls to the {@link #mutate} method. Negative k are treated as k=0.
   * @throws IllegalArgumentException if a ≥ b or if p is negative.
   */
  public UndoableRandomValueChangeMutation(int a, int b, double p, int k) {
    super(a, b, p, k);
  }

  @Override
  public void mutate(T c) {
    if (c.length() > 0) {
      if (oldA == null || oldA.length < c.length()) oldA = new int[c.length()];
      restorableMutate(c, oldA);
    }
  }

  @Override
  public void undo(T c) {
    if (c.length() > 0) {
      restore(c, oldA);
    }
  }

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

  @Override
  public boolean equals(Object other) {
    return super.equals(other) && other instanceof UndoableRandomValueChangeMutation;
  }
}