Recombination Distributions for Genetic Algorithms

Abstract Though genetic algorithms are loosely based on the principles of genetic variation and natural selection, the theory of mathematical genetics has not played a large role in most analyses of genetic algorithms. This paper reviews some well known results in mathematical genetics that use probability distributions to characterize the effects of recombination on multiple loci in the absence of selection. The relevance of this characterization to genetic algorithm research is illustrated by using it to quantify certain inductive biases associated with crossover operators. The potential significance of this work for the theory of genetic algorithms is discussed.

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