Steady-state and generational selection methods with evolution strategies were compared on several test functions with respect to their performance and efficiency. The evaluation was carried out for a parallel computing environment with a particular focus on heterogeneous calculation times for the assessment of the individual fitness. This set-up was motivated by typical tasks in design optimization. Our results show that steady-state methods outperform classical generational selection for highly variable evaluation time or for small degrees of parallelism. The 2D turbine blade optimization results did not allow a clear conclusion about the advantage of steady-state selection, however this is coherent with the above findings. © Springer-Verlag 2004.
CITATION STYLE
Enache, R., Sendhoff, B., Olhofer, M., & Hasenjäger, M. (2004). Comparison of steady-state and generational evolution strategies for parallel architectures. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3242, 253–262. https://doi.org/10.1007/978-3-540-30217-9_26
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