Explaining Adaptation in Genetic Algorithms With Uniform Crossover: The Hyperclimbing Hypothesis
This addresses a theoretical gap for researchers in evolutionary computation by providing a novel explanation for algorithm behavior, though it appears incremental as it builds on existing genetic algorithm frameworks.
The paper tackles the problem of explaining adaptation in genetic algorithms with uniform crossover by proposing the hyperclimbing hypothesis, which suggests these algorithms work by implementing an efficient non-local search heuristic, and experimental results show a simple tweak based on this hypothesis dramatically improves performance on large random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin Glasses problem.
The hyperclimbing hypothesis is a hypothetical explanation for adaptation in genetic algorithms with uniform crossover (UGAs). Hyperclimbing is an intuitive, general-purpose, non-local search heuristic applicable to discrete product spaces with rugged or stochastic cost functions. The strength of this heuristic lie 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 holds that UGAs work by implementing efficient hyperclimbing. Proof of concept for this hypothesis comes from the use of a novel analytic technique involving the exploitation of algorithmic symmetry. We have also obtained experimental results that show 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.