Machine Learning Methods to Analyze Migration Parameters in Parallel Genetic Algorithms

Parallel genetic algorithms (PGA) are a powerful tool to deal with complex optimization problems. Nevertheless, additional parameters are required to configure properly their performance. The task to select these 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 a preliminary analysis on some of the PGA parameters, like migration frequency, topology, connectivity and number of islands and population sizes. The study has been carried out after an intensive set of experiments to 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 approach to generalize extracted patterns from different problems into a configuration hints and suggested parameters.