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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.reals;

import org.cicirello.math.rand.RandomSampler;
import org.cicirello.search.operators.CrossoverOperator;
import org.cicirello.search.representations.RealVector;

/**
 * Implementation of K-point crossover, but for RealVectors. K-point crossover is a generalization
 * of two-point crossover. In a K-point crossover, K random cross points are chosen uniformly along
 * the length of the parents. Both parents are cut at the K cross points, breaking the parents into
 * multiple segments. The elements within every other segment are then swapped between the two
 * parents to form the two children. K-point crossover originated for crossover of bit vectors
 * within a genetic algorithm, but has been adapted here for vectors of doubles.
 *
 * @param  The specific RealVector type, such as {@link RealVector} or {@link
 *     org.cicirello.search.representations.BoundedRealVector BoundedRealVector}.
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class KPointCrossover implements CrossoverOperator {

  private final int[] indexes;

  /**
   * Constructs a K-point crossover operator.
   *
   * @param k The number of cross points, which must be at least 1. Although, if you want only a
   *     single cross point, you should instead use {@link SinglePointCrossover}; and likewise, if
   *     you only want 2 cross points, you should instead use {@link TwoPointCrossover}.
   * @throws IllegalArgumentException if k is less than 1
   */
  public KPointCrossover(int k) {
    if (k < 1) {
      throw new IllegalArgumentException("Must specify at least k=1 cross points");
    }
    indexes = new int[k];
  }

  /**
   * {@inheritDoc}
   *
   * Behavior is undefined if the RealVectors are of different lengths.
   *
   * @throws IllegalArgumentException if c1.length() is less than k.
   */
  @Override
  public void cross(RealVector c1, RealVector c2) {
    RandomSampler.sample(c1.length(), indexes.length, indexes);
    sort(indexes);
    int i = 1;
    for (; i < indexes.length; i += 2) {
      RealVector.exchange(c1, c2, indexes[i - 1], indexes[i] - 1);
    }
    i--;
    if (i < indexes.length) {
      RealVector.exchange(c1, c2, indexes[i], c1.length() - 1);
    }
  }

  @Override
  public KPointCrossover split() {
    // Need to construct a fresh instance.
    // Maintains state that cannot be shared.
    return new KPointCrossover(indexes.length);
  }

  private void sort(int[] indexes) {
    // Indexes should probably be small, and might be sorted or nearly sorted
    // depending upon which sampling method Random.sample chose... e.g., for small
    // k relative to length, the alg it chooses will return indexes sorted (though
    // that is not part of the contract so can't assume that to be the case.
    //
    // For small length, especially if sorted or nearly sorted, an insertion sort
    // though quadratic worst case will likely be faster than an n log n sort.
    // If sorted or nearly sorted will run in linear time. Otherwise, due to
    // probably short length since k is likely small, will probably be faster
    // than mergesort due to effects of constants.
    for (int i = 1; i < indexes.length; i++) {
      int element = indexes[i];
      int j = i - 1;
      while (j >= 0 && indexes[j] > element) {
        indexes[j + 1] = indexes[j];
        j--;
      }
      indexes[j + 1] = element;
    }
  }
}