Nonlinearity, Hyperplane Ranking and the Simple Genetic Algorithm
نویسندگان
چکیده
Several metrics are used in empirical studies to explore the mechanisms of convergence of genetic algorithms. The metric is designed to measure the consistency of an arbitrary ranking of hyperplanes in a partition with respect to a target string. Walsh coeecients can be calculated for small functions in order to characterize sources of linear and nonlinear interactions. A simple deception measure is also developed to look closely at the eeects of increasing nonlinearity of functions. Correlations between the metric and deception measure are discussed and relationships between and convergence behavior of a simple genetic algorithm are studied over large sets of functions with varying degrees of nonlinearity.
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