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
EditorsXin Yao, Jong-Hwan Kim, Takeshi Furuhashi
PublisherSpringer Verlag
Pages206-215
Number of pages10
ISBN (Print)3540633995, 9783540633990
DOIs
Publication statusPublished - 1997
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
Country/TerritoryKorea, Republic of
CityTaejon
Period96/11/996/11/12

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1997.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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