Optimization By Decimation: Explaining Adaptation in Genetic Algorithms with Uniform Crossover

I’m preparing chapter 4 of my dissertation for submission to a journal.

Manuscript: http://s3.amazonaws.com/burjorjee/www/hyperclimbing_hypothesis.pdf

Abstract:
We submit the hyperclimbing hypothesis—an explanation for adaptation in genetic algorithms with uniform crossover (UGAs). Hyperclimbing is a stochastic search heuristic that works by decimating a search space, i.e. by iteratively fixing the values of small numbers of search space attributes. Global decimation is known to be an effective way to approach large instances of hard constraint satisfaction problems. The hyperclimbing hypothesis holds that UGAs work by implicitly implementing efficient global decimation. Proof of concept for this hypothesis comes from the use of a novel analytic technique involving the exploitation of algorithmic symmetry. We also present experimental results that show that a simple tweak inspired by the hyperclimbing hypothesis significantly improves the performance of a UGA on an instance of Uniform Random MAX-3SAT . The hyperclimbing hypothesis suggests that other kinds of evolutionary algorithms may also work by implicitly implementing efficient global decimation.

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