Abstract
Despite its known shortcomings, penalty function approaches are among the most commonly used constraint handling methods in the field of evolutionary computation. In this paper, we argue that some of the techniques used to alleviate these shortfalls (namely scaling and normalisation) cannot avoid undesired search biases. Instead, we introduce the notion of desired search biases to effectively solve problems with a large number of competing constraints. The methods using this notion are based on dominance comparison by lexicographic ordering of objectives. For the real-world problem we use, two of the methods outperform the best performing penalty function approach by finding feasible solutions repeatedly.
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Notes
- 1.
This also works for minimisation problems, but in this paper and unless otherwise stated we assume optimisation means maximisation of the fitness (and objective/CV) function.
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Schellenberg, S., Li, X., Michalewicz, Z. (2017). Preliminary Study on Solving Coal Processing and Blending Problems Using Lexicographic Ordering. In: Peng, W., Alahakoon, D., Li, X. (eds) AI 2017: Advances in Artificial Intelligence. AI 2017. Lecture Notes in Computer Science(), vol 10400. Springer, Cham. https://doi.org/10.1007/978-3-319-63004-5_18
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DOI: https://doi.org/10.1007/978-3-319-63004-5_18
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