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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

GaMeDE2 — improved Gap–based Memetic Differential Evolution applied to multi–modal optimisation

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DOI: http://dx.doi.org/10.15439/2022F153

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 291–300 ()

Full text

Abstract. This paper presents an improved Gap--based Memetic Differential Evolution (GaMeDE2), the modification of the GaMeDE method, which took second place in the GECCO 2020 Competition on Niching Methods for Multi-modal Optimization. GaMeDE2 has reduced complexity, fewer parameters, redefined initialisation, selection operator, and removed processing phases. The method is verified using standard benchmark function sets (classic ones and CEC2013) and a newly proposed benchmark set comprised of deceptive functions. A detailed comparison to state-of-the-art methods (like HVCMO and SDLCSDE) is presented, where the proposed GaMeDE2 outperforms or gives similar results to other methods. The document is concluded by discussing various insights on the problem instances and the methods created as a part of the research.

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