Abstract
Parallel genetic algorithms (PGAs) are a powerful tool to deal with complex optimization problems. Nevertheless, the task of selecting its parameters accurately is an optimization problem by itself. Any additional help or hints to adjust the configuration parameters will lead both towards a more efficient PGA application and to a better comprehension on how these parameters affect optimization behavior and performance. This contribution offers an analysis on certain PGA parameters such as migration frequency, topology, connectivity and number of islands. The study has been carried out on an intensive set of experiments that collect PGA performance on several representative problems. The results have been analyzed using machine learning methods to identify behavioral patterns that are labeled as “good” PGA configurations. This study is a first step to generalize relevant patterns from the problems analyzed that identify better configurations in PGAs.
This work was supported by the Madrid Regional Education Ministry and the European Social Fund.
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Muelas, S., Peña, J.M., Robles, V., La Torre, A., de Miguel, P. (2007). Machine Learning to Analyze Migration Parameters in Parallel Genetic Algorithms. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_27
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DOI: https://doi.org/10.1007/978-3-540-74972-1_27
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