Abstract
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.
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Enache, R., Sendhoff, B., Olhofer, M., Hasenjäger, M. (2004). Comparison of Steady-State and Generational Evolution Strategies for Parallel Architectures. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_26
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DOI: https://doi.org/10.1007/978-3-540-30217-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
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