Supporting polyploidy in genetic algorithms using dominance vectors
By memorizing alleles that have been successful in the past, polyploidy has been found to be beneficial for adapting to changing environments. This paper explores the benefits of using polyploidy in Genetic Algorithms. Polyploidy is provided in our approach by using a local chromosome to reflect dominance in diploid and tetraploid organisms, with and without evolving crossover points, added to provide linkage between chromosomes and the dominance control vector. We compare our polyploid approach to a haploid implementation for a benchmark that involves a 0/1 knapsack problem with time varying weight constraints.
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