Hybrid evolutionary learning of fuzzy logic and genetic algorithm

Sung Bae Cho, Seung Ik Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a hybrid method of fuzzy logic and genetic algorithm as promising model for evolutionary system, which controls a mobile robot effectively. The system obtains sensory information from eight infrared sensors and operates the robot with two motors driven by fuzzy inference based on the sensory information. Genetic algorithm has been utilized to robustly determine the shape and number of membership functions in fuzzy rules. Through the simulation with a simulated robot called Khepera, we assure ourselves that the evolutionary approach finds a set of optimal fuzzy rules to make the robot reach the goal point, as well as to solve autonomously several subproblems such as obstacle avoidance and passing-by narrow corridors.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 1st Asia-Pacific Conference, SEAL 1996, Selected Papers
PublisherSpringer Verlag
Pages206-215
Number of pages10
ISBN (Print)3540633995, 9783540633990
Publication statusPublished - 1997 Jan 1
Event1st Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1996 - Taejon, Korea, Republic of
Duration: 1996 Nov 91996 Nov 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1285
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1996
CountryKorea, Republic of
CityTaejon
Period96/11/996/11/12

Fingerprint

Evolutionary Learning
Hybrid Learning
Fuzzy Logic
Fuzzy logic
Robot
Genetic algorithms
Genetic Algorithm
Fuzzy rules
Robots
Fuzzy Rules
Infrared Sensor
Obstacle Avoidance
Fuzzy Inference
Fuzzy inference
Collision avoidance
Membership functions
Hybrid Method
Membership Function
Mobile Robot
Mobile robots

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Cho, S. B., & Lee, S. I. (1997). Hybrid evolutionary learning of fuzzy logic and genetic algorithm. In Simulated Evolution and Learning - 1st Asia-Pacific Conference, SEAL 1996, Selected Papers (pp. 206-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1285). Springer Verlag.
Cho, Sung Bae ; Lee, Seung Ik. / Hybrid evolutionary learning of fuzzy logic and genetic algorithm. Simulated Evolution and Learning - 1st Asia-Pacific Conference, SEAL 1996, Selected Papers. Springer Verlag, 1997. pp. 206-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Cho, SB & Lee, SI 1997, Hybrid evolutionary learning of fuzzy logic and genetic algorithm. in Simulated Evolution and Learning - 1st Asia-Pacific Conference, SEAL 1996, Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1285, Springer Verlag, pp. 206-215, 1st Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1996, Taejon, Korea, Republic of, 96/11/9.

Hybrid evolutionary learning of fuzzy logic and genetic algorithm. / Cho, Sung Bae; Lee, Seung Ik.

Simulated Evolution and Learning - 1st Asia-Pacific Conference, SEAL 1996, Selected Papers. Springer Verlag, 1997. p. 206-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1285).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Cho SB, Lee SI. Hybrid evolutionary learning of fuzzy logic and genetic algorithm. In Simulated Evolution and Learning - 1st Asia-Pacific Conference, SEAL 1996, Selected Papers. Springer Verlag. 1997. p. 206-215. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).