Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms

Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)

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Ari Juels, Martin Wattenberg


We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinato(cid:173) rial function optimizers. In particular, we address two problems to which GAs have been applied in the literature: Koza's ll-multiplexer problem and the jobshop problem. We demonstrate that simple stochastic hill(cid:173) climbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these two problems. We further illustrate, in the case of the jobshop problem, how insights ob(cid:173) tained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.