Package net.sf.tweety.math.opt.solver

Class SimpleGeneticOptimizationSolver

java.lang.Object

net.sf.tweety.math.opt.Solver

net.sf.tweety.math.opt.solver.SimpleGeneticOptimizationSolver

public class SimpleGeneticOptimizationSolver
extends Solver

This class implements a simple genetic optimization algorithm for solving optimization problems with box or equality constraints on float variables. Note that equality constraints are encoded in the target function!

Author:

Matthias Thimm

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
private class	SimpleGeneticOptimizationSolver.FitnessComparator	Compares individuals by the fitness (value of the target function)

Field Summary

Fields

Modifier and Type	Field	Description
private static org.slf4j.Logger	log	Logger
private int	minIterations	The minimal number of iterations.
private int	populationIncreaseCrossOver	How many new individuals are created by cross-over (per pair of individuals)
private int	populationIncreaseMutation	How many new individuals are created by mutation (per individual)
private int	populationSize	The size of the population
private double	precision	If an iteration improves less than this value the algorithm ends.
private java.util.Random	rand	For randomization
private static double	VAR_CROSSOVER_PROB	The probability of taking the average value of a variable in the crossover step.
private static double	VAR_MUTATE_PROB	The probability of changing the value of a variable in the mutation step.
private static double	VAR_MUTATE_STRENGTH	The magnitude of changing the value of a variable in the mutation step.
private static IntegerConstant	VERY_LARGE_NUMBER	A very large number for encoding constraints in the target function.

Constructor Summary

Constructors

Constructor	Description
SimpleGeneticOptimizationSolver(int populationSize, int populationIncreaseMutation, int populationIncreaseCrossOver, int minIterations, double precision)	Creates a new simple genetic optimization solver.

Method Summary

Modifier and Type	Method	Description
private java.util.Map<FloatVariable,Term>	crossover(java.util.Map<FloatVariable,Term> ind1, java.util.Map<FloatVariable,Term> ind2)	Makes a crossover of the two individuals
static boolean	isInstalled()
private java.util.Map<FloatVariable,Term>	mutate(java.util.Map<FloatVariable,Term> ind)	Mutates the given individual
java.util.Map<Variable,Term>	solve(ConstraintSatisfactionProblem problem)	Computes a solution to the given constraint satisfaction or optimization problem, i.e.
java.util.Map<Variable,Term>	solve(Term t, int optimization_objective)	Returns the variable assignment that maximizes/minimizes the given term (which only contains variables with defined upper and lower bounds).

Methods inherited from class net.sf.tweety.math.opt.Solver

getDefaultGeneralSolver, getDefaultIntegerLinearSolver, getDefaultLinearSolver, hasDefaultGeneralSolver, hasDefaultIntegerLinearSolver, hasDefaultLinearSolver, setDefaultGeneralSolver, setDefaultIntegerLinearSolver, setDefaultLinearSolver

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

log

private static org.slf4j.Logger log

Logger

VAR_MUTATE_PROB

private static final double VAR_MUTATE_PROB

The probability of changing the value of a variable in the mutation step.

See Also:

Constant Field Values

VAR_MUTATE_STRENGTH

private static final double VAR_MUTATE_STRENGTH

The magnitude of changing the value of a variable in the mutation step.

See Also:

Constant Field Values

VAR_CROSSOVER_PROB

private static final double VAR_CROSSOVER_PROB

The probability of taking the average value of a variable in the crossover step.

See Also:

Constant Field Values

VERY_LARGE_NUMBER

private static final IntegerConstant VERY_LARGE_NUMBER

A very large number for encoding constraints in the target function.

rand

private java.util.Random rand

For randomization

populationSize

private int populationSize

The size of the population

populationIncreaseMutation

private int populationIncreaseMutation

How many new individuals are created by mutation (per individual)

populationIncreaseCrossOver

private int populationIncreaseCrossOver

How many new individuals are created by cross-over (per pair of individuals)

precision

private double precision

If an iteration improves less than this value the algorithm ends.

minIterations

private int minIterations

The minimal number of iterations.

Constructor Detail

SimpleGeneticOptimizationSolver

public SimpleGeneticOptimizationSolver(int populationSize,
                                       int populationIncreaseMutation,
                                       int populationIncreaseCrossOver,
                                       int minIterations,
                                       double precision)

Creates a new simple genetic optimization solver.

Parameters:

populationSize - The size of the population

populationIncreaseMutation - How many new individuals are created by mutation (per individual)

populationIncreaseCrossOver - How many new individuals are created by cross-over (per pair of individuals)

minIterations - The minimal number of iterations

precision - If an iteration improves less than this value the algorithm ends

Method Detail

solve

public java.util.Map<Variable,Term> solve(ConstraintSatisfactionProblem problem)
                                         throws GeneralMathException

Description copied from class: Solver

Computes a solution to the given constraint satisfaction or optimization problem, i.e. a mapping from variables of the problem to terms.

Specified by:

solve in class Solver

Parameters:

problem - the actual problem

Returns:

a mapping from variables of the problem to terms.

Throws:

GeneralMathException - if something went wrong.

mutate

private java.util.Map<FloatVariable,Term> mutate(java.util.Map<FloatVariable,Term> ind)

Mutates the given individual

Parameters:

ind - some individual

Returns:

a new individual

crossover

private java.util.Map<FloatVariable,Term> crossover(java.util.Map<FloatVariable,Term> ind1,
                                                          java.util.Map<FloatVariable,Term> ind2)

Makes a crossover of the two individuals

Parameters:

ind1 - some individual

ind2 - some individual

Returns:

a new individual

solve

public java.util.Map<Variable,Term> solve(Term t,
                                                int optimization_objective)
                                         throws GeneralMathException

Returns the variable assignment that maximizes/minimizes the given term (which only contains variables with defined upper and lower bounds).

Parameters:

t - the term to be evaluated

optimization_objective - one of OptimizationProblem.MAXIMIZE, OptimizationProblem.MINIMIZE

Returns:

the optimal variable assignment

Throws:

GeneralMathException - if some issue occured during computation.

isInstalled

public static boolean isInstalled()
                           throws java.lang.UnsupportedOperationException

Throws:

java.lang.UnsupportedOperationException