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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;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import org.cicirello.math.rand.RandomIndexer;

/**
 * A WeightedHybridMutation enables using multiple mutation operators for the search, such that each
 * time the {@link #mutate} method is called, a randomly chosen mutation operator is applied to the
 * candidate solution. The random choice of mutation operator is weighted proportionately based on
 * an array of weights passed upon construction. This implementation supports the {@link #undo}
 * method.
 *
 * Consider the following weights: w = [ 1, 2, 3]. In this example, the first mutation operator
 * will be used with probability 0.167, the second mutation operator will be used with probability
 * 2/6 = 0.333, and the third mutation operator will be used with probability 3/6 = 0.5.
 *
 * @param  The type of object used to represent candidate solutions to the problem.
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class WeightedHybridUndoableMutation implements UndoableMutationOperator {

  private final ArrayList> mutationOps;
  private int last;
  private final int[] choice;

  /**
   * Constructs a WeightedHybridUndoableMutation from a Collection of UndoableMutationOperator.
   *
   * @param mutationOps A Collection of UndoableMutationOperator.
   * @param weights The array of weights, whose length must be equal to mutationOps.size(). Every
   *     element of weights must be greater than 0.
   * @throws IllegalArgumentException if mutationOps doesn't contain any UndoableMutationOperator.
   * @throws IllegalArgumentException if mutationOps.size() is not equal to weights.length.
   * @throws IllegalArgumentException if any weights are non-positive.
   */
  public WeightedHybridUndoableMutation(
      Collection> mutationOps, int[] weights) {
    if (mutationOps.size() == 0)
      throw new IllegalArgumentException("Must pass at least 1 UndoableMutationOperator.");
    if (mutationOps.size() != weights.length)
      throw new IllegalArgumentException(
          "Number of weights must be same as number of mutation operators.");
    choice = weights.clone();
    if (choice[0] <= 0) throw new IllegalArgumentException("The weights must be positive.");
    for (int i = 1; i < choice.length; i++) {
      if (choice[i] <= 0) throw new IllegalArgumentException("The weights must be positive.");
      choice[i] = choice[i - 1] + choice[i];
    }
    this.mutationOps = new ArrayList>(mutationOps.size());
    for (UndoableMutationOperator op : mutationOps) {
      this.mutationOps.add(op);
    }
    last = -1;
  }

  /*
   * private constructor to support split method
   */
  private WeightedHybridUndoableMutation(WeightedHybridUndoableMutation other) {
    mutationOps = new ArrayList>(other.mutationOps.size());
    for (UndoableMutationOperator op : other.mutationOps) {
      mutationOps.add(op.split());
    }
    last = -1;
    choice = other.choice.clone();
  }

  @Override
  public void mutate(T c) {
    int value = RandomIndexer.nextInt(choice[choice.length - 1]);
    last = Arrays.binarySearch(choice, value);
    if (last < 0) last = -(last + 1);
    else last++;
    mutationOps.get(last).mutate(c);
  }

  @Override
  public void undo(T c) {
    if (last >= 0) mutationOps.get(last).undo(c);
  }

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