Abstract
Database Knowledge Discovery attention has been growing last decades using different approaches as part of a new era when information is multiplied in proportion and importance. Fuzzy Logic predicates approach is one of them, fundamental because of their interpretability properties. A new concept of transdisciplinary interpretability has been introduced by using a new axiomatic approach: Compensatory Fuzzy Logic. Several ways have been used as fuzzy predicates searching techniques, notably a Genetic Algorithm, part of a Data Analysis Platform called Eureka Universe. This paper presents two Genetic Programming Algorithm Approaches, with outstanding results and illustrated by a case study.
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Llorente-Peralta, C.E., Cruz-Reyes, L., Espín-Andrade, R.A. (2021). Knowledge Discovery Using an Evolutionary Algorithm and Compensatory Fuzzy Logic. In: Castillo, O., Melin, P. (eds) Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-68776-2_21
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