A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms

A robust, inherently parallel genetic algorithm is proposed for the problem of configuring solvers automatically and a gender separation is introduced to cope with the high costs of evaluating the fitness of individuals.

Abstract

A problem that is inherent to the development and efficient use of solvers is that of tuning parameters. The CP community has a long history of addressing this task automatically. We propose a robust, inherently parallel genetic algorithm for the problem of configuring solvers automatically. In order to cope with the high costs of evaluating the fitness of individuals, we introduce a gender separation whereby we apply different selection pressure on both genders. Experimental results on a selection of SAT solvers show significant performance and robustness gains over the current state-of-the-art in automatic algorithm configuration.