Module org.cicirello.chips_n_salsa
Package org.cicirello.search.evo

Class InverseCostFitnessFunction<T extends Copyable<T>>

java.lang.Object
org.cicirello.search.evo.InverseCostFitnessFunction<T>
Type Parameters:
T - The type of object under optimization.
All Implemented Interfaces:
FitnessFunction<T>, FitnessFunction.Double<T>
public final class InverseCostFitnessFunction<T extends Copyable<T>> extends Object implements FitnessFunction.Double<T>
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 OptimizationProblem or 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 FitnessProportionalSelection and 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 OptimizationProblem and 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 IllegalArgumentException.

  • Constructor Details

    • InverseCostFitnessFunction

      public InverseCostFitnessFunction(OptimizationProblem<T> problem)
      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()).
      Parameters:
      problem - The optimization problem.
      Throws:
      IllegalArgumentException - if problem.minCost() is non-finite, such as infinite or nan.

    • InverseCostFitnessFunction

      public InverseCostFitnessFunction(IntegerCostOptimizationProblem<T> problem)
      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()).
      Parameters:
      problem - The optimization problem.
      Throws:
      IllegalArgumentException - if problem.minCost() equals Integer.MAX_VALUE or Integer.MIN_VALUE.

    • InverseCostFitnessFunction

      public InverseCostFitnessFunction(OptimizationProblem<T> problem, double c)
      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()).
      Parameters:
      problem - The optimization problem.
      c - A constant which must be positive.
      Throws:
      IllegalArgumentException - if c is less than or equal to 0.0.
      IllegalArgumentException - if problem.minCost() is non-finite, such as infinite or nan.

    • InverseCostFitnessFunction

      public InverseCostFitnessFunction(IntegerCostOptimizationProblem<T> problem, double c)
      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()).
      Parameters:
      problem - The optimization problem.
      c - A constant which must be positive.
      Throws:
      IllegalArgumentException - if c is less than or equal to 0.0.
      IllegalArgumentException - if problem.minCost() equals Integer.MAX_VALUE or Integer.MIN_VALUE.

  • Method Details

    • fitness

      public double fitness(T candidate)
      Description copied from interface: FitnessFunction.Double
      Computes the fitness of a candidate solution to a problem, for use by genetic algorithms and other evolutionary algorithms.
      Specified by:
      fitness in interface FitnessFunction.Double<T extends Copyable<T>>
      Parameters:
      candidate - The solution whose fitness is to be evaluated.
      Returns:
      the fitness of candidate

    • getProblem

      public Problem<T> getProblem()
      Description copied from interface: FitnessFunction
      Gets a reference to the problem that this fitness function is for.
      Specified by:
      getProblem in interface FitnessFunction<T extends Copyable<T>>
      Returns:
      a reference to the problem.