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Cosmetics.

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This commit is contained in:
Marcel Kronfeld 2011-04-08 07:48:11 +00:00
parent 877118b6b3
commit aeda5a83a3
3 changed files with 16 additions and 21 deletions
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11
src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
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@ -16,11 +16,14 @@ import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG; import eva2.tools.math.RNG;
/** This is an implementation of the CHC Adaptive Search Algorithm by Eselman. It is /** This is an implementation of the CHC Adaptive Search Algorithm by Eshelman. It is
* limited to binary data and is based on massively distruptive crossover. I'm not * limited to binary data and is based on massively disruptive crossover. I'm not
* shure whether i've implemented this correctly, but i definetly wasn't able to make * sure whether i've implemented this correctly, but i definitely wasn't able to make
* it competitive to a standard GA.. *sigh* * it competitive to a standard GA.. *sigh*
* This is a implementation of the CHC Apative Search Algorithm. * This is a implementation of the CHC Adaptive Search Algorithm (Cross generational
* elitist selection, Heterogeneous recombination and Cataclysmic mutation).
* Citation:
*
* Copyright: Copyright (c) 2003 * Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture * Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert * @author Felix Streichert

17
src/eva2/server/go/strategies/EvolutionaryProgramming.java
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@ -2,7 +2,6 @@ package eva2.server.go.strategies;
import eva2.server.go.InterfacePopulationChangedEventListener; import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual; import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.GAIndividualBinaryData;
import eva2.server.go.operators.selection.InterfaceSelection; import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectEPTournaments; import eva2.server.go.operators.selection.SelectEPTournaments;
import eva2.server.go.populations.InterfaceSolutionSet; import eva2.server.go.populations.InterfaceSolutionSet;
@ -14,6 +13,8 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
/** Evolutionary programming by Fogel. Works fine but is actually a quite greedy local search /** Evolutionary programming by Fogel. Works fine but is actually a quite greedy local search
* strategy solely based on mutation. To prevent any confusion, the mutation rate is temporaily * strategy solely based on mutation. To prevent any confusion, the mutation rate is temporaily
* set to 1.0. * set to 1.0.
* Potential citation: the PhD thesis of David B. Fogel (1992).
*
* Copyright: Copyright (c) 2003 * Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture * Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert * @author Felix Streichert
@ -74,20 +75,6 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
population.incrGeneration(); population.incrGeneration();
} }
/** This method will assign fitness values to all individual in the
* current population.
* @param population The population that is to be evaluated
*/
private void defaultEvaluatePopulation(Population population) {
GAIndividualBinaryData tmpIndy;
for (int i = 0; i < population.size(); i++) {
tmpIndy = (GAIndividualBinaryData) population.get(i);
tmpIndy.SetFitness(0, tmpIndy.defaultEvaulateAsMiniBits());
population.incrFunctionCalls();
}
population.incrGeneration();
}
/** This method will generate the offspring population from the /** This method will generate the offspring population from the
* given population of evaluated individuals. * given population of evaluated individuals.
*/ */

9
src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java
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@ -13,10 +13,15 @@ import eva2.server.go.problems.AbstractOptimizationProblem;
import eva2.server.go.problems.B1Problem; import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem; import eva2.server.go.problems.InterfaceOptimizationProblem;
/** Population based increamental learning in the PSM by Mommarche /** Population based incremental learning in the PSM by Monmarche
* version with also allows to simulate ant systems due to the flexible * version with also allows to simulate ant systems due to the flexible
* update rule of V. But both are limited to binary gentypes. * update rule of V. But both are limited to binary genotypes.
* This is a simple implementation of Population Based Incremental Learning. * This is a simple implementation of Population Based Incremental Learning.
*
* Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles Venturini:
* On the similarities between AS, BSC and PBIL: toward the birth of a new meta-heuristic.
* TecReport 215. Univ. de Tours, 1999.
*
* Copyright: Copyright (c) 2003 * Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture * Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert * @author Felix Streichert