Explaining Adaptation in Genetic Algorithms With Uniform Crossover

نویسنده

  • Keki M Burjorjee
چکیده

Hyperclimbing is an intuitive, general-purpose, global optimization heuristic applicable to product spaces with rugged or stochastic cost functions. The strength of this heuristic lies in its insusceptibility to local optima when the cost function is deterministic, and its tolerance for noise when the cost function is stochastic. Hyperclimbing works by decimating a search space, i.e., by iteratively fixing the values of small numbers of variables. The hyperclimbing hypothesis posits that genetic algorithms with uniform crossover (UGAs) work by implementing efficient hyperclimbing. Proof of concept for this hypothesis comes from the use of an analytic technique that exploits algorithmic symmetry. Additionally, we present experimental results showing that a simple tweak inspired by the hyperclimbing hypothesis dramatically improves the performance of a UGA on large, random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin Glasses problem.

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تاریخ انتشار 2012