Defining Fuzzy Measures: A Comparative Study with Genetic and Gradient Descent Algorithms

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Due to limitations of classical weighted average aggregation operators, there is an increase usage of fuzzy integrals, like the Sugeno and Choquet integrals, as alternative aggregation operators. However, their applicability has been threatened by the crux of determining the fuzzy measures in real problems. One way to determine these measures is by using learning data and optimizing the parameters. In this paper we made a comparative study of two well known optimization algorithms, Genetic Algorithm and Gradient Descent to determine fuzzy measures. Two illustrative cases are used to compare the algorithms and assess their performance.
Original languageUnknown
Title of host publicationIntelligent Engineering Systems and Computational Cybernetics
PublisherSpringer Netherlands
Pages427-437
ISBN (Print)-
DOIs
Publication statusPublished - 1 Jan 2009

Cite this

DEE Group Author, & Ribeiro, M. R. S. D. A. (2009). Defining Fuzzy Measures: A Comparative Study with Genetic and Gradient Descent Algorithms. In Intelligent Engineering Systems and Computational Cybernetics (pp. 427-437). Springer Netherlands. https://doi.org/10.1007/978-1-4020-8678-6_37