A quantitative analysis of evolvability for an evolutionary fuzzy logic controller

Seung Ik Lee, Sung Bae Cho

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper presents a quantitative analysis of evolvability with evolutionary activity statistics in an evolutionary fuzzy system. In general, one can estimate the performance of an evolved fuzzy controller by its fitness. However, it is difficult to explain how its fitness or adaptability has been obtained. Evolutionary activity is used to measure the evolvability of fuzzy rules and explain why salient rules have higher evolvability. A genetic algorithm is used to construct a fuzzy logic controller for a mobile robot in simulation environments. The quantitative analysis shows that sufficient evolvability is maintained during the evolution and that it contributes to the construction of the optimal controller.

Original languageEnglish
Pages (from-to)369-385
Number of pages17
JournalIntegrated Computer-Aided Engineering
Volume10
Issue number4
Publication statusPublished - 2003 Oct 31

Fingerprint

Evolvability
Fuzzy Logic Controller
Quantitative Analysis
Fuzzy logic
Controllers
Chemical analysis
Fitness
Fuzzy rules
Fuzzy systems
Mobile robots
Simulation Environment
Fuzzy Controller
Fuzzy Rules
Adaptability
Genetic algorithms
Statistics
Mobile Robot
Fuzzy Systems
Genetic Algorithm
Sufficient

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Artificial Intelligence

Cite this

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A quantitative analysis of evolvability for an evolutionary fuzzy logic controller. / Lee, Seung Ik; Cho, Sung Bae.

In: Integrated Computer-Aided Engineering, Vol. 10, No. 4, 31.10.2003, p. 369-385.

Research output: Contribution to journalArticle

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