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GA ISB done (but not tested)
Signed-off-by: facaraff <[email protected]>
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‎src/algorithms/specialOptions/BIAS/CMAES.java

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/**
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Copyright (c) 2019, Fabio Caraffini ([email protected], [email protected])
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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1. Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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The views and conclusions contained in the software and documentation are those
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of the authors and should not be interpreted as representing official policies,
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either expressed or implied, of the FreeBSD Project.
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*/
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package algorithms.specialOptions.BIAS;
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import static utils.algorithms.Misc.generateRandomSolution;

‎src/algorithms/specialOptions/BIAS/DE.java

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int newID = 0;
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int bestID = -1;
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writeHeader(" pop "+populationSize+" F "+F+" CR "+CR+" alpha "+alpha, problem);
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writeHeader(" popSize "+populationSize+" F "+F+" CR "+CR+" alpha "+alpha, problem);
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String line = new String();
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‎src/algorithms/specialOptions/BIAS/GA.java

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package algorithms.specialOptions.BIAS;
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import static utils.algorithms.operators.ISBOp.generateRandomSolution;
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import static utils.algorithms.operators.ISBOp.GAmutations;
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import static utils.algorithms.operators.ISBOp.GAParentSelections;
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import static utils.algorithms.operators.ISBOp.GACrossovers;
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import utils.algorithms.Counter;
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import static utils.MatLab.indexMin;
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import static utils.MatLab.indexMax;
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import interfaces.AlgorithmBias;
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import interfaces.Problem;
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import static utils.RunAndStore.FTrend;
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public class GA extends AlgorithmBias
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{
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private char selectionStrategy = 't'; // r --> fitness proportional roulette wheel t-->stochastic tournament
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private char crossoverStrategy = 'a'; //d-->discrete a-->full arithmetic
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private char mutationStrategy = 'g'; // c --> Cauchy g-->Gaussian
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@Override
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public FTrend execute(Problem problem, int maxEvaluations) throws Exception
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{
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int populationSize = getParameter("p0").intValue();
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int nt = getParameter("p1").intValue();
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double CR = getParameter("p2").doubleValue();
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double d = getParameter("p3").doubleValue(); //fixed to 0.25 in Kononova 2015
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double md = getParameter("p4").doubleValue(); //fixed to ; 0.01 in Kononova 2015
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FTrend FT = new FTrend();
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int problemDimension = problem.getDimension();
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double[][] bounds = problem.getBounds();
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int[] ids = new int[populationSize];
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double[][] population = new double[populationSize][problemDimension];
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double[] fitnesses = new double[populationSize];
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String FullName = getFullName("GA"+this.mutationStrategy+this.crossoverStrategy+this.selectionStrategy+this.correction,problem);
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Counter PRNGCounter = new Counter(0);
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createFile(FullName);
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int newID = 0;
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String line = new String();
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if(this.selectionStrategy == 't')
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line+=" popSize "+populationSize+" nt "+nt;
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else if(this.selectionStrategy == 'r')
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line+=" popSize "+populationSize;
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else
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System.out.println("Unrecognised selection stratgy!");
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if(this.crossoverStrategy == 'a')
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line+=" d "+d;
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else if(this.crossoverStrategy == 'd')
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line+=" CR "+CR;
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else
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System.out.println("Unrecognised crossover stratgy!");
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if(this.mutationStrategy == 'c')
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line+=" md "+md;
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else if(this.mutationStrategy == 'g')
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line+=" md "+md;
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else
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System.out.println("Unrecognised mutation stratgy!");
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writeHeader(line, problem);
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line = new String();
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double[] best = new double[problemDimension];
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double fBest = Double.NaN;
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int i = 0;
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int parent1 = 0;
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int parent2 = 0;
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int worst = 0;
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// evaluate initial population
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for (int j = 0; j < populationSize; j++)
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{
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double[] tmp = generateRandomSolution(bounds, problemDimension, PRNGCounter);
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for (int n = 0; n < problemDimension; n++)
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population[j][n] = tmp[n];
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fitnesses[j] = problem.f(population[j]);
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i++;
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newID++;
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ids[j] = newID;
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line =""+newID+" -1 "+"-1 "+formatter(fitnesses[j])+" "+i+" -1";
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for(int n = 0; n < problemDimension; n++)
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line+=" "+formatter(population[j][n]);
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line+="\n";
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bw.write(line);
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line = null;
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line = new String();
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if (j == 0 || fitnesses[j] < fBest)
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{
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fBest = fitnesses[j];
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for (int n = 0; n < problemDimension; n++)
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best[n] = population[j][n];
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FT.add(i, fBest);
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}
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}
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// iterate
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while (i < maxEvaluations)
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{
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parent1 = GAParentSelections( selectionStrategy, fitnesses, nt, PRNGCounter);
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parent2 = GAParentSelections( selectionStrategy, fitnesses, nt,PRNGCounter);
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double[] child = GACrossovers(population[parent1], population[parent2], CR, d, crossoverStrategy, PRNGCounter);
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child = GAmutations(child, mutationStrategy, md, bounds, PRNGCounter);
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child = correct(child,population[parent1],bounds);
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double fChild = problem.f(child);
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i++;
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worst = indexMax(fitnesses);
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if(fChild<fitnesses[worst])
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{
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newID++;
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int indexWorst = ids[worst];
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int indexParent1 = ids[parent1];
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int indexParent2 = ids[parent2];
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ids[worst] = newID;
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for(int n=0; n<problemDimension; n++)
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population[worst][n] = child[n];
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fitnesses[worst] = fChild;
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line =""+newID+" "+indexParent1+" "+indexParent2+" "+formatter(fChild)+" "+i+" "+indexWorst;
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for(int n = 0; n < problemDimension; n++)
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line+=" "+formatter(child[n]);
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line+="\n";
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bw.write(line);
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line = null;
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line = new String();
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}
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}
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int ib = indexMin(fitnesses);
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finalBest = population[ib];
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fBest = fitnesses[ib];
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FT.add(i, fBest);
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closeAll();
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writeStats(FullName, (double) this.numberOfCorrections/maxEvaluations, PRNGCounter.getCounter(), "correctionsGA");
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return FT;
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}
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protected void setSelectionStrategy(char ss) {this.selectionStrategy = ss;} // r --> fitness proportional roulette wheel t-->stochastic tournament
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protected void setCrossoverStrategy(char cs) {this.crossoverStrategy = cs;} //d-->discrete a-->full arithmetic
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protected void setMutationStrategy(char ms) {this.mutationStrategy = ms;} // c --> Cauchy g-->Gaussian
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public GA() {}
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public GA(char ss,char cs, char ms){super(); setSelectionStrategy(ss); setCrossoverStrategy(cs); setMutationStrategy(ms);}
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}

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