Genetic evolution of control systems

Mu Song Chen, Tze Yee Ho, Chipan Hwang

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

1 Citation (Scopus)

Abstract

In this paper, we present to utilize Genetic Algorithms (GAs) as tools to model control processes. Two different crossover operators are combined during evolution to maintain population diversity and to sustain local improvement in the search space. In this manner, a balance between global exploration and local exploitation is reserved during genetic search. To verify the efficiency of the proposed method, the desired control sequences of a given system are solved by the optimal control theory as well as GA with hybrid crossovers to compare their performances. The experimental results showed that the control sequences obtained from the proposed GA with hybrid crossovers are quite consistent with the results of the optimal control.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
Pages284-292
Number of pages9
EditionPART 2
DOIs
Publication statusPublished - 2013 Oct 7
Event4th International Conference on Advances in Swarm Intelligence, ICSI 2013 - Harbin, China
Duration: 2012 Jun 122012 Jun 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7929 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Advances in Swarm Intelligence, ICSI 2013
CountryChina
CityHarbin
Period12-06-1212-06-15

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chen, M. S., Ho, T. Y., & Hwang, C. (2013). Genetic evolution of control systems. In Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings (PART 2 ed., pp. 284-292). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7929 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-38715-9-34