Hyperplane Ranking in Simple Genetic Algorithms
نویسندگان
چکیده
We examine the role of hyperplane ranking during genetic search by developing a metric for measuring the degree of ranking that exists with respect to static hyperplane averages taken directly from the function, as well as the dynamic ranking of hyperplanes during genetic search. The metric applied to static rankings subsumes the concept of deception but the metric provides a more precise characterization of a function. We show that the degree of dynamic ranking induced by a simple genetic algorithm is highly correlated with the degree of static ranking that is inherent in the function, especially during the initial generations of search.
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