Abstract
In this paper we consider the Differential Evolution (DE) algorithm by using fuzzy logic to make dynamic changes in the mutation (F) and crossover (Cr) parameters separately, and this modification of the algorithm we can call it the Fuzzy Differential Evolution algorithm (FDE). A comparison of the FDE algorithm using type-1 fuzzy logic and interval type-2 fuzzy logic is performed for a set of Benchmark functions.
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We would like to express our appreciation to CONACYT and Tijuana Institute of Technology for the support provided to this research work.
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Ochoa, P., Castillo, O., Soria, J. (2019). Interval Type-2 Fuzzy Logic Dynamic Mutation and Crossover Parameter Adaptation in a Fuzzy Differential Evolution Method. In: Hadjiski, M., Atanassov, K. (eds) Intuitionistic Fuzziness and Other Intelligent Theories and Their Applications. Studies in Computational Intelligence, vol 757. Springer, Cham. https://doi.org/10.1007/978-3-319-78931-6_5
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DOI: https://doi.org/10.1007/978-3-319-78931-6_5
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