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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Yu, T.-L., Goldberg, D.E., Yassine, A., & Chen, Y.-P., (2003), A genetic algorithm design inspired by organization theory: a pilot study of a dependency structure matrix driven genetic algorithm. (IlliGAL Technical Report No. 2003007). Urbana, IL: University of Illinois at Urbana-Champaign.
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© 2003 Springer-Verlag Berlin Heidelberg
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Yu, TL., Goldberg, D.E., Yassine, A., Chen, YP. (2003). Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_54
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DOI: https://doi.org/10.1007/3-540-45110-2_54
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