Abstract
This paper proposes a hybrid evolutionary algorithm (EA) dealing with population-based incremental learning (PBIL) and some efficient local search strategies. A simple PBIL using real codes is developed. The evolutionary direction and approximate gradient operators are integrated to the main procedure of PBIL. The method is proposed for single objective global optimization. The search performance of the developed hybrid algorithm for box-constrained optimization is compared with a number of well-established and newly developed evolutionary algorithms and meta-heuristics. It is found that, with the given optimization settings, the proposed hybrid optimizer outperforms the other EAs. The new derivative-free algorithm can maintain outstanding abilities of EAs.
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Bureerat, S. (2011). Hybrid Population-Based Incremental Learning Using Real Codes. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_28
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DOI: https://doi.org/10.1007/978-3-642-25566-3_28
Publisher Name: Springer, Berlin, Heidelberg
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