Implementing Evolutionary Self-Organizing Maps with the Genetic Operations of Graph Evolution Theory

Maiga Chang, Jia Sheng Heh

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper analyzes the genetic operations of a new evolution mechanism proposed by us for improving the capability to deal with graph-form solutions in the real world of genetic algorithms based on the theories of GAs and GPs. A prototype of graph evolution with genetic operations is implemented and applied to some graph-related systems with the Irish-student classification data. Evaluation between conventional optimization mechanisms and graph evolution theory is also made for proving the advantage of using graph evolution. Be notable is the graph evolution theory proposed in this paper can cover most applications of GAs and GPs.

Original languageEnglish
Pages462-467
Number of pages6
Publication statusPublished - 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: 20 Jul. 200324 Jul. 2003

Conference

ConferenceInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR
Period20/07/0324/07/03

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