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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, and Ying-Ping Chen
University of Illinois at Urbana-Champaign
Urbana, IL 61801
{tianliyu,deg,yassine,ychen21}@uiuc.edu
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.
LNCS 2724, p. 1620 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003