A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms
Conference paper
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.
Keywords
Genetic Algorithm Training Instance Iterate Local Search Parallel Genetic Algorithm Linear Genetic Program
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