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RSA Laboratories

Hillclimbing as a Baseline Method for the Evaluation of Stochastic Approximation Algorithms

Ari Juels and Marty Wattenberg

Citation: A. Juels and M. Wattenberg. Hillclimbing as a Baseline Method for the Evaluation of Stochastic Optimization Algorithms. In David S. Touretzky et al., editors, Advances in Neural Information Processing Systems 8, pages 430-436, MIT Press, 1995.

Abstract: We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza's 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.

Note: This is essentially an empirical demonstration of why GAs and the literature surrounding them are mostly rubbish.

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