Achieving highly scalable evolutionary real-valued optimization by exploiting partial evaluations
In: Evolutionary Computation, Jg. 29 (2020), Heft 1, S. 129-155
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Zugriff:
It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, for example, non-separable, multimodal, and multiobjective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. GOMEA was recently shown to be extendable to real-valued optimization through a combination with the real-valued estimation of distribution algorithm AMaLGaM. In this article, we definitively introduce the Real-Valued GOMEA (RV-GOMEA), and introduce a new variant, constructed by combining GOMEA with what is arguably the best-known real-valued EA, the Covariance Matrix Adaptation Evolution Strategies (CMA-ES). Both variants of GOMEA are compared to L-BFGS and the Limited Memory CMA-ES (LM-CMA-ES). We show that both variants of RV-GOMEA achieve excellent performance and scalability in a GBO setting, which can be orders of magnitude better than that of EAs unable to efficiently exploit the GBO setting.
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Achieving highly scalable evolutionary real-valued optimization by exploiting partial evaluations
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Autor/in / Beteiligte Person: | Bouter, Anton ; Alderliesten, Tanja ; Bosman, Peter A. N. ; Radiotherapy |
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Zeitschrift: | Evolutionary Computation, Jg. 29 (2020), Heft 1, S. 129-155 |
Veröffentlichung: | 2020 |
Medientyp: | unknown |
ISSN: | 1063-6560 (print) |
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