Abstract
This paper investigates the off-line use of the dependency structure matrix genetic algorithm (DSMGA). In particular, a problem-specific crossover operator is design by performing dependency structure matrix (DSM) analysis. The advantages and disadvantages of such an off-line use are discussed. Two schemes that helps the off-line usage are proposed. Finally, those off-line schemes are demonstrated by DSMGA on MaxTrap functions.
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References
Yu, T.-L., Goldberg, D.E., Yassine, A., Chen, Y.-p.: Genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm. In: Proceedings of Artificial Neural Networks in Engineering 2003 (ANNIE 2003), pp. 327–332 (2003) (Also IlliGAL Report No. 2003007)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E.: The design of innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Boston (2002)
Steward, D.V.: The design structure system: A method for managing the design of complex systems. IEEE Transactions on Engineering Management 28, 74–77 (1981)
Yassine, A., Falkenburg, D.R., Chelst, K.: Engineering design management: An information structure approach. International Journal of production research 37, 2957–2975 (1999)
Yu, T.-L., Yassine, A., Goldberg, D.E.: A genetic algorithm for developing modular product architectures. In: Proceedings of the ASME 2003 International Design Engineering Technical Conferences, 15th International Conference on Design Theory and Methodology, DETC 2003 (2003) DTM–48657 (Also IlliGAL Report No. 2003024)
Pelikan, M., Goldberg, D.E., Lobo, F.G.: A survey of optimization by building and using probabilistic models. IlliGAL Report No. 99018, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL (1999)
Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms. Kluwer Academic Publishers, Dordrecht (2002)
Sharman, D., Yassine, A., Carlile, P.: Characterizing modular architectures. In: ASME 14th International Conference, DTM-34024 (2002)
Munetomo, M., Goldberg, D.E.: Identifying linkage groups by nonlinearity/nonmonotonicity detection. In: Proceedings of the Genetic and Evolutionary Computation Conference 1999, vol. 1, pp. 433–440 (1999)
Thierens, D., Goldberg, D.E.: Convergence models of genetic algorithm selection schemes. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 119–129. Springer, Heidelberg (1994)
Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3, 493–530 (1989)
Kargupta, H.: The performance of the gene expression messy genetic algorithm on real test functions. In: Proceedings of 1996 IEEE International Conference on Evolutionary Computation, pp. 631–636 (1996)
Smith, J., Fogarty, T.C.: Recombination strategy adaptation via evolution of gene linkage. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 826–831 (1996)
Harik, G.R., Goldberg, D.E.: Learning linkage. Foundations of Genetic Algorithms 4, 247–262 (1996)
Pelikan, M.: Bayesian optimization algorithm: From single level to hierarchy. Ph.d. dissertation, University of Illinois at Urbana-Champaign (2002) (Also IlliGAL Report No. 2002023)
Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm: I. Continuous parameter optimization. Evolutionary Computation 1, 25–49 (1993)
Ceroni, A., Pelikan, M., Goldberg, D.E.: Convergence-time models for the simple genetic algorithm with finite population. IlliGAL Report No. 2001028, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL (2001)
Harik, G., Cantú-Paz, E., Goldberg, D.E., Miller, B.L.: The gambler’s ruin problem, genetic algorithms, and the sizing of populations. In: Proceedings of 1997 IEEE International Conference on Evolutionary Computation, pp. 7–12 (1997)
Goldberg, D.E., Deb, K., Clark, J.H.: Genetic algorithms, noise, and the sizing of populations. Complex Systems 6, 333–362 (1992)
Yu, T.-L., Goldberg, D.E.: Toward an understanding of the quality and efficiency of model building for genetic algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference 2004 (2004) (to appear) (Also IlliGAL Report No. 2004004)
Simon, H.A.: The Sciences of the Artificial. The MIT Press, Cambridge (1968)
Harik, G.R., Lobo, F.G.: A parameter-less genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference 1999, vol. 1, pp. 258–265 (1999)
Sastry, K., Goldberg, D.E.: Analysis of mixing in genetic algorithms: A survey. IlliGAL Report No. 2002012, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL (2002)
Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. Foundations of Genetic Algorithms 2, 93–108 (1993)
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Yu, TL., Goldberg, D.E. (2004). Dependency Structure Matrix Analysis: Offline Utility of the Dependency Structure Matrix Genetic Algorithm. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_31
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DOI: https://doi.org/10.1007/978-3-540-24855-2_31
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