Combination and Recombination in Genetic Algorithms

Recombination is supposed to enable the component characteristics from two parents to be extracted and then reassembled in different combinations – hopefully producing an offspring that has the good characteristics of both parents. This can work only if it is possible to identify which parts of each parent should be extracted. Crossover in the standard GA, for example, takes subsets of genes that are adjacent on the genome. Other variations of the GA propose more sophisticated methods for identifying good subsets of genes within an individual. Our approach is different; rather than devising methods to enable successful extraction of gene subsets from the parents, we utilize variable size individuals which represent subsets of genes from the outset. By allowing each individual to represent a building-block explicitly, the normal action of selection can identify good building-blocks without also promoting garbage genes. Then putting together two individuals (creating an offspring that is twice the size), straight forwardly produces the sum of the parents characteristics. This process is more properly combination than recombination since these building blocks have not been previously combined with any other. This paper summarizes our research on this approach and describes improved methods that reduce the domain knowledge required for successful application.

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