Choice of effective fitness functions for genetic algorithm-aided dynamic fuzzy rule interpolation

Nitin Kumar Naik, Ren Diao, Qiang Shen

Research output: Contribution to conferencePaperpeer-review


Fuzzy rule interpolation (FRI) has been a vital reasoning tool for sparse
fuzzy rule-based systems. Throughout interpolative reasoning, an FRI
system may produce a large number of interpolated rules, which generally serve no further purpose once the required outcomes have been obtained. How- ever, this abandoned pool of interpolated rules can be used to improve the existing sparse rule base, because they contain useful information on the underlying problem domain. Efficient extraction of knowledge from such a pool of interpolated rules are indeed helpful to analyse and update the sparse rule base, leading to a dynamic sparse fuzzy rule base for building an enhanced fuzzy system. Following this idea, a genetic algorithm (GA) based dynamic fuzzy rule interpolation framework has been proposed recently. This paper presents an extension of the dynamic FRI system. In particular, it investigates different fitness functions and their effects on the outcomes of the GA-based system. A
variety of fitness functions based on cluster quality indices are employed
and tested, including Dunn Index, Davies- Boulding Index, Ball-Hall Index
and BetaCV Index. Experimental investigation demonstrates that results
obtained by the use of Dunn index or Davies-Bouldin index are better than
those by Ball-Hall or BetaCV index, with those using Davies-Bouldin
index performing the best overall. Such results offer an empirical
guideline for the selection of the fitness function in implementing accurate
GA-based dynamic FRI systems
Original languageEnglish
Number of pages1
Publication statusPublished - Aug 2015
EventFuzzy Systems - Istanbul, Turkey
Duration: 02 Aug 201505 Aug 2015
Conference number: 24


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2015
Period02 Aug 201505 Aug 2015


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