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Vasanth Raja Chittampally 10IT05F 1 Genetic Algorithms Genetic Programming Representation of Chromosome Selection.

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Presentation on theme: "Vasanth Raja Chittampally 10IT05F 1 Genetic Algorithms Genetic Programming Representation of Chromosome Selection."— Presentation transcript:

1 Vasanth Raja Chittampally 10IT05F 1 Genetic Algorithms Genetic Programming Representation of Chromosome Selection Procedure(pseudo code) Roulette Wheel procedure Java Genetic Algorithm library Python Genetic Algorithm library

2 Representation of Chromosome private static class Chromosome { – public double score; – StringBuffer chromo= new StringBuffer(chromoLen * 4); – // – public Chromosome(int target) { // chromo.append(binString); // } Vasanth Raja Chittampally 2

3 Roulette Wheel selection procedure A roulette wheel contains slots weighted in proportion to string fitness values. In the below code we see the select function returns the index value corresponding to the selected individual. Partial sum of the fitness values is accumulated in the real variable partsum – rand=rand*sumfitness Sum of the population fitnesses is multiplied by the normalized pseudorandom number. Repeate-until searches through the weighted roulette wheel until the partial sum is greater than or equal to the stopping point rand. 3

4 Pseudo code of Selection process Function select(popsize, sumfitness, population) { – Begin Partsum=0 j=0 rand= rand*sumfitness Repeat –j=j+1 –partsum=partsum+pop[j].fitness Until(partsum>=rand) or (j=popsize) –Return individual number –Select=j end Vasanth Raja Chittampally 4

5 Java Selection Function private Chomosone selectMember(ArrayList l) { double tot=0.0; for (int x=l.size()-1;x>=0;x--) { double score = ((Chomosone)l.get(x)).score; tot+=score; } double rand1 = tot*rand.nextDouble(); double ttot=0.0; for (int x=l.size()-1;x>=0;x--) { Chomosone node = (Chomosone)l.get(x); ttot+=node.score; if (ttot>=rand1) { l.remove(x); return node; } return (Chomosone)l.remove(l.size()-1); } 5

6 Java Genetic Algorithm Library It provides basic genetic mechanisms that can be easily used to apply evolutionary principles to problem solutions This contains the general purpose functions to be performed for Genetic algorithms Good documentation is available Set of examples were given in the above link with source code 6

7 Python Genetic Algorithms Library Pyevolve was developed to be a complete genetic algorithm framework written in pure python. Good documentation is available Set of examples were given in the above link with source code Python based genetic algorithms library. 7

8 References sorting-bogosort-using-pyevolve/ sorting-bogosort-using-pyevolve/ sorting-bogosort-using-pyevolve/ sorting-bogosort-using-pyevolve/ 12-the-travelling-salesman-problem-tsp 12-the-travelling-salesman-problem-tsp 8

9 Queries ??? Vasanth Raja Chittampally 10IT05F 9

10 Thank you Vasanth Raja Chittampally 10IT05F 10


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