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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].

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
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