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.
This work was partly supported by the projects TIN2007-68005-C04-04 and TIN2006-15662-C02-02 funded by the MEC, and by the the National Science Foundation through the Career: Cornflower Project (award number 0644113).
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Ansótegui, C., Sellmann, M., Tierney, K. (2009). A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms. In: Gent, I.P. (eds) Principles and Practice of Constraint Programming - CP 2009. CP 2009. Lecture Notes in Computer Science, vol 5732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04244-7_14
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DOI: https://doi.org/10.1007/978-3-642-04244-7_14
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