Genetic algorithm design inspired by organizational theory: Pilot study of dependency structure matrix driven genetic algorithm

Tian Li Yu*, David E. Goldberg, Ali A. Yassine, Ying-Ping Chen

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

31 Scopus citations

Abstract

This paper proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix clustering to extract building block (BB) information and use the information to accomplish BB-wise crossover. Three cases: tight, loose, and random linkage, are tested on both a DSMDGA and a simple genetic algorithm (SGA). Experiments showed that the DSMDGA is able to correctly identify BBs and outperforms a SGA by using the extracted BB information.

Original languageEnglish
Pages327-332
Number of pages6
StatePublished - 1 Dec 2003
EventSmart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life - Proceedings of the Artificial Neural Networks in Engineering Conference - St. Louis, MO., United States
Duration: 2 Nov 20035 Nov 2003

Conference

ConferenceSmart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life - Proceedings of the Artificial Neural Networks in Engineering Conference
CountryUnited States
CitySt. Louis, MO.
Period2/11/035/11/03

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