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

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

*Corresponding author for this work

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

This study proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) 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.

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