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

Constructor Summary

Constructors

Constructor and 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 and Description
static boolean	isInstalled()
Map<Variable,Term>	solve(ConstraintSatisfactionProblem problem) Computes a solution to the given constraint satisfaction or optimization problem, i.e.
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

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 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.

solve

public 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

isInstalled

public static boolean isInstalled()
                           throws UnsupportedOperationException

Throws:

UnsupportedOperationException