A multi-objective genetic algorithm for tuning and rule selection to obtain accurate and compact linguistic fuzzy rule-based systems

被引：92

作者：

Alcala, R. ^{[1
]}

Gacto, M. J.

Herrera, F.

Alcala-Fdez, J.

机构：

[1] Univ Granada, Dept Comp Sci, E-18071 Granada, Spain

[2] Univ Jaen, Dept Comp Sci, E-23071 Jaen, Spain

来源：

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS | 2007年 / 15卷 / 05期

关键词：

multi-objective genetic algorithms; linguistic modelling; interpretability-accuracy trade-off; rule selection; tuning of membership functions;

D O I：

10.1142/S0218488507004868

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work proposes the application of Multi-Objective Genetic Algorithms to obtain Fuzzy Rule-Based Systems with a better trade-off between interpretability and accuracy in linguistic fuzzy modelling problems. To do that, we present a new post-processing method that by considering selection of rules together with the tuning of membership functions gets solutions only in Pareto zone with the highest accuracy. This method is based on the well-known SPEA2 algorithm, applying approriate genetic operators and including some modifications to concentrate the search in the desired Pareto zone.

引用

页码：539 / 557

页数：19

共 50 条

[1] Hybrid learning models to get the interpretability-accuracy trade-off in fuzzy modeling [J].