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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.ss;

import org.cicirello.search.problems.Problem;
import org.cicirello.util.Copyable;

/**
 * Classes implementing this interface are used as constructive heuristics for constructing
 * heuristic solutions to optimization problems, as well as for certain stochastic sampling search
 * algorithms.
 *
 * @param  The type of Partial object for which this ConstructiveHeuristic guides construction,
 *     which is assumed to be an object that is a sequence of integers (e.g., vector of integers,
 *     permutation, or some other indexable type that stores integers).
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public interface ConstructiveHeuristic> {

  /**
   * Heuristically evaluates the possible addition of an element to the end of a Partial. Higher
   * evaluations imply that the element is a better choice for the next element to add. For example,
   * if you evaluate two elements, x and y, with h, and h returns a higher value for y than for x,
   * then this means that y is believed to be the better choice according to the heuristic.
   * Implementations of this interface must ensure that h always returns a positive result. This is
   * because stochastic sampling algorithms such as HBSS and VBSS assume that the constructive
   * heuristic returns only positive values.
   *
   * @param p The current state of the Partial
   * @param element The element under consideration for adding to the Partial
   * @param incEval An IncrementalEvaluation of p. This method assumes that incEval is of the same
   *     runtime type as the object returned by {@link #createIncrementalEvaluation}.
   * @return The heuristic evaluation of the hypothetical addition of element to the end of p. The
   *     higher the evaluation, the more important the heuristic believes that element should be
   *     added next. The intention is to compare the value returned with the heuristic evaluations
   *     of other elements. Individual results in isolation are not necessarily meaningful.
   * @throws ClassCastException if incEval is not of the same runtime type as the objects returned
   *     by the {@link #createIncrementalEvaluation} method of the class implementing this interface
   */
  double h(Partial p, int element, IncrementalEvaluation incEval);

  /**
   * Creates an IncrementalEvaluation object corresponding to an initially empty Partial for use in
   * incrementally constructing a solution to the problem for which this heuristic is designed. The
   * object returned incrementally computes any data associated with a Partial as needed by the
   * {@link #h} method. The {@link #h} method will assume that it will be given an object of the
   * specific runtime type returned by this method. It is unsafe to pass IncrementalEvaluation
   * objects created by one heuristic to the {@link #h} method of another.
   *
   * The default implementation simply returns null, which is appropriate for heuristics that
   * won't benefit from incrementally computing heuristic information.
   *
   * @return An IncrementalEvaluation for an empty Partial to be used for incrementally computing
   *     any data required by the {@link #h} method.
   */
  default IncrementalEvaluation createIncrementalEvaluation() {
    return null;
  }

  /**
   * Creates an empty Partial solution, which will be incrementally transformed into a complete
   * solution of a specified length.
   *
   * @param n the desired length of the final complete solution.
   * @return an empty Partial solution
   */
  Partial createPartial(int n);

  /**
   * Gets the required length of complete solutions to the problem instance for which this
   * constructive heuristic is configured.
   *
   * @return length of solutions to the problem instance for which this heuristic is configured
   */
  int completeLength();

  /**
   * Gets a reference to the instance of the optimization problem that is the subject of this
   * heuristic.
   *
   * @return the instance of the optimization problem that is the subject of this heuristic.
   */
  Problem getProblem();
}