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

/**
 * This class provides a convenient mechanism for transforming optimization cost values to fitness
 * values. Most of the algorithms in the library require a cost function to minimize through a class
 * that implements either {@link OptimizationProblem} or {@link IntegerCostOptimizationProblem}.
 * However, the evolutionary algorithms in the library require a fitness function such that higher
 * fitness implies better solution. Furthermore, some selection operators further assume that
 * fitness values are positive, such as {@link FitnessProportionalSelection} and {@link
 * StochasticUniversalSampling}.
 *
 * This class transforms the cost of solution s to fitness to meet these requirements with the
 * following transformation: fitness(s) = c / (c + problem.cost(s) - problem.minCost()), where c is
 * a positive constant, which defaults to 1.0 (see constructors). The problem.cost(s) and
 * problem.minCost() refer to the methods by those names in the {@link OptimizationProblem} and
 * {@link IntegerCostOptimizationProblem} classes. Note that the adjustment by problem.minCost()
 * ensures that fitness will be positive even if costs can be negative.
 *
 * Note that the problem's implementation of minCost must return a finite lower bound for the
 * cost function (which is assumed to be correct), otherwise the constructors of this class will
 * throw an {@link IllegalArgumentException}.
 *
 * @param  The type of object under optimization.
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class InverseCostFitnessFunction>
    implements FitnessFunction.Double {

  private final Problem problem;
  private final double c;
  private final double adjustment;

  /**
   * Constructs a fitness function that transforms the cost of solution s to fitness with the
   * following transformation: fitness(s) = 1.0 / (1.0 + problem.cost(s) - problem.minCost()).
   *
   * @param problem The optimization problem.
   * @throws IllegalArgumentException if problem.minCost() is non-finite, such as infinite or nan.
   */
  public InverseCostFitnessFunction(OptimizationProblem problem) {
    this(problem, 1.0);
  }

  /**
   * Constructs a fitness function that transforms the cost of solution s to fitness with the
   * following transformation: fitness(s) = 1.0 / (1.0 + problem.cost(s) - problem.minCost()).
   *
   * @param problem The optimization problem.
   * @throws IllegalArgumentException if problem.minCost() equals Integer.MAX_VALUE or
   *     Integer.MIN_VALUE.
   */
  public InverseCostFitnessFunction(IntegerCostOptimizationProblem problem) {
    this(problem, 1.0);
  }

  /**
   * Constructs a fitness function that transforms the cost of solution s to fitness with the
   * following transformation: fitness(s) = c / (c + problem.cost(s) - problem.minCost()).
   *
   * @param problem The optimization problem.
   * @param c A constant which must be positive.
   * @throws IllegalArgumentException if c is less than or equal to 0.0.
   * @throws IllegalArgumentException if problem.minCost() is non-finite, such as infinite or nan.
   */
  public InverseCostFitnessFunction(OptimizationProblem problem, double c) {
    if (c <= 0.0) {
      throw new IllegalArgumentException("c must be positive");
    }
    adjustment = problem.minCost();
    if (!java.lang.Double.isFinite(adjustment)) {
      throw new IllegalArgumentException(
          "Only supports problems that provide finite cost lower bound via minCost method.");
    }
    this.c = c;
    this.problem = problem;
  }

  /**
   * Constructs a fitness function that transforms the cost of solution s to fitness with the
   * following transformation: fitness(s) = c / (c + problem.cost(s) - problem.minCost()).
   *
   * @param problem The optimization problem.
   * @param c A constant which must be positive.
   * @throws IllegalArgumentException if c is less than or equal to 0.0.
   * @throws IllegalArgumentException if problem.minCost() equals Integer.MAX_VALUE or
   *     Integer.MIN_VALUE.
   */
  public InverseCostFitnessFunction(IntegerCostOptimizationProblem problem, double c) {
    if (c <= 0.0) {
      throw new IllegalArgumentException("c must be positive");
    }
    int m = problem.minCost();
    if (m == java.lang.Integer.MAX_VALUE || m == java.lang.Integer.MIN_VALUE) {
      throw new IllegalArgumentException(
          "Only supports problems that provide finite cost lower bound via minCost method.");
    }
    adjustment = m;
    this.c = c;
    this.problem = problem;
  }

  @Override
  public double fitness(T candidate) {
    return c / (c + problem.costAsDouble(candidate) - adjustment);
  }

  @Override
  public Problem getProblem() {
    return problem;
  }
}