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

DEE Group Author; Maria Rita Sarmento de Almeida Ribeiro

doi:10.1007/978-1-4020-8678-6_37

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

DEE Group Author, Maria Rita Sarmento de Almeida Ribeiro

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	Unknown
Title of host publication	Intelligent Engineering Systems and Computational Cybernetics
Publisher	Springer Netherlands
Pages	427-437
ISBN (Print)	-
DOIs	https://doi.org/10.1007/978-1-4020-8678-6_37
Publication status	Published - 1 Jan 2009

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10.1007/978-1-4020-8678-6_37

Cite this

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title = "Defining Fuzzy Measures: A Comparative Study with Genetic and Gradient Descent Algorithms",

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.",

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AB - 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.

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