Yu, Tian-Li; Goldberg, David E.; Yassine, Ali; Chen, Ying-Ping Genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm. (English) Zbl 1038.68933 Cantú-Paz, Erick (ed.) et al., Genetic and evolutionary computation – GECCO 2003. Genetic and evolutionary computation conference, Chicago, IL, USA, July 12–16, 2003. Proceedings, Part II. Berlin: Springer (ISBN 3-540-40603-4/pbk). Lect. Notes Comput. Sci. 2724, 1620-1621 (2003). Summary: This study 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.For the entire collection see [Zbl 1025.68695]. Cited in 1 Document MSC: 68U99 Computing methodologies and applications 68T05 Learning and adaptive systems in artificial intelligence 68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) Software:AS 136 PDFBibTeX XMLCite \textit{T.-L. Yu} et al., Lect. Notes Comput. Sci. 2724, 1620--1621 (2003; Zbl 1038.68933) Full Text: Link