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The Role Of Selection In Genetic Algorithms

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The Role of Selection in Genetic Algorithms
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Source code given as wellIt should be noted, however, that natural selection process does not merely choose the top x number of solutions; the solutions are instead chosen statistically such that it is more likely that a solution with a higher fitness value will be chosen, but it is not guaranteed.This taking up of the entire population by one extremely fit solution is known as premature convergence and is an undesirable condition in a GAThe higher ranked individuals are preferred more than the lower ranked onesThe fitness function is evaluated for each individual, providing fitness values, which are then normalizedThis can be simulated by following algorithmAt higher fitness values, this problem is even more exaggerated

If this procedure is repeated until there are enough selected individuals, this selection method is called fitness proportionate selection or roulette-wheel selectionSelection is the stage of a genetic algorithm in which individual genomes are chosen from a population for later breeding (using the crossover operator)This is because they don't make use of single-point transition rules to move from one single instance in the solution space to anotherAll rights reservedIntroduction

Tournament Selection is also extremely popular in literature as it can even work with negative fitness valuesOf course, step 1 is performed only once for each populationThe previous selection will have problems when the fitnesses differs very much[JGAP Home]A generic selection procedure may be implemented as follows:Calculate S = the sum of a finesses 07f867cfac

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