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A Grammatical Evolution Based Automated Configuration of an Ensemble Differential Evolution Algorithm

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Pattern Recognition and Machine Intelligence (PReMI 2023)

Abstract

Designing/configuring ensemble Differential Evolution (DE) algorithms with complementary search characteristics is a complex problem requiring both in-depth understanding of the constituent algorithm’s dynamics and tacit knowledge. This paper proposes a Grammatical Evolution (GE) based automated configuration of a recent ensemble DE algorithm - Improved Multi-population Ensemble Differential Evolution (IMPEDE). A Backus Naur Form grammar, with nine ensemble and DE parameters, has been designed to represent all possible IMPEDE configurations. The proposed approach has been employed to evolve IMPEDE configurations that solve CEC’17 benchmark optimization problems. The evolved configurations have been validated on CEC’14 suite and a real-world optimization problem - economic load dispatch (ELD) problem - from CEC’11 suite. The simulation experiments demonstrate that the proposed approach is capable of evolving IMPEDE configurations that exhibit statistically superior or comparable performance against the manual configuration of IMPEDE as well as against other prominent ensemble DE algorithms.

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Correspondence to C. Shunmuga Velayutham .

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Indu, M.T., Velayutham, C.S. (2023). A Grammatical Evolution Based Automated Configuration of an Ensemble Differential Evolution Algorithm. In: Maji, P., Huang, T., Pal, N.R., Chaudhury, S., De, R.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2023. Lecture Notes in Computer Science, vol 14301. Springer, Cham. https://doi.org/10.1007/978-3-031-45170-6_61

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  • DOI: https://doi.org/10.1007/978-3-031-45170-6_61

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