Abstract
In this paper, we present a hyper-heuristic, based on Differential Evolution, for solving constrained optimization problems. Differential Evolution has been found to be a very effective and efficient optimization algorithm for continuous search spaces, which motivated us to adopt it as our search engine for dealing with constrained optimization problems. In our proposed hyper-heuristic, we adopt twelve differential evolution models for our low-level heuristic.We also adopt four selection mechanisms for choosing the low-level heuristic. The proposed approach is validated using a well-known benchmark for constrained evolutionary optimization. Results are compared with respect to those obtained by a state-of-theart constrained differential evolution algorithm (CDE) and another hyper-heuristic that adopts a random descent selection mechanism. Our results indicate that our proposed approach is a viable alternative for dealing with constrained optimization problems.
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Villela Tinoco, J.C., Coello Coello, C.A. (2013). hypDE: A Hyper-Heuristic Based on Differential Evolution for Solving Constrained Optimization Problems. In: Schütze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31519-0_17
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DOI: https://doi.org/10.1007/978-3-642-31519-0_17
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