Multi-stage genetic algorithm learning in game playing

Chuen-Tsai Sun*, Ming Da Wu

*Corresponding author for this work

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

In this paper, we explore the concept of genetic structural expansion by employing a multi-stage chromosome coding scheme in a genetic algorithm (GA) based game-playing environment. Although structural expansion has been considered as a means of increasing diversity so as to benefit the GA optimization process in a changing world, it was seldom studied in the context of a multi-stage reinforced environment. This paper compares three chromosome coding schemes: monoploidy, triploidy (as a special case of polyploidy), and structural expansion, and discusses their impacts on multiple fuzzy-staged game-playing strategies. We show in this paper that when polyploid chromosomes are employed to cope with the changing environment in the domain of game-playing, the average learning result is apparently better than the learning curves in which only monoploidy is used. This piece of work is an empirical study under the Evolutionary Strategy paradigm mentioned in [7].

Original languageEnglish
Pages223-227
Number of pages5
StatePublished - 1 Dec 1994
EventProceedings of the 1994 1st International Joint Conference of NAFIPS/IFIS/NASA - San Antonio, TX, USA
Duration: 18 Dec 199421 Dec 1994

Conference

ConferenceProceedings of the 1994 1st International Joint Conference of NAFIPS/IFIS/NASA
CitySan Antonio, TX, USA
Period18/12/9421/12/94

Fingerprint Dive into the research topics of 'Multi-stage genetic algorithm learning in game playing'. Together they form a unique fingerprint.

  • Cite this

    Sun, C-T., & Wu, M. D. (1994). Multi-stage genetic algorithm learning in game playing. 223-227. Paper presented at Proceedings of the 1994 1st International Joint Conference of NAFIPS/IFIS/NASA, San Antonio, TX, USA, .