Kanta Premji Vekaria

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The performance of a genetic algorithm (GA) is dependent on many factors: the type of crossover operator, the rate of crossover, the rate of mutation, population size, and the encoding used are just a few examples. Currently, GA practitioners pick and choose GA parameters empirically until they achieve adequate performance for a given problem. In this paper(More)
Previous studies have shown that hitchhiking in standard GAs can cause premature convergence. In this study we show that hitchhiking also occurs in adaptive operators, specifically, selective crossover an adaptive recombination operator. We compare selective crossover with uniform crossover (a highly disruptive operator) and show that although selective(More)
This paper presents a new crossover operator for genetic programming – dominance crossover. Dominance crossover is similar to the use of dominance in nature. In nature, dominance is used as a genotype to phenotype mapping when an organism carries pairs (or more than one) chromosome, but here we use dominance on a haploid structure. The haploid form contains(More)
Recombination operators with high positional bias are less disruptive against adjacent genes. Therefore, it is ideal for the encoding to position epistatic genes adjacent to each other and aid GA search through genetic linkage. To produce an encoding that facilitates genetic linkage is problematic. This study focuses on selective crossover, which is an(More)
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