Abstract
This paper introduces subchromosome representations to the linkage learning genetic algorithm (LLGA). The subchromosome representation is utilized for effectively lowering the number of building blocks in order to escape from the performance limit implied by the convergence time model for the linkage learning genetic algorithm. A preliminary implementation to realize subchromosome representations is developed and tested. The experimental results indicate that the proposed representation can improve the performance of the linkage learning genetic algorithm on uniformly scaled problems, and the initial implementation provides a potential way for the linkage learning genetic algorithm to incorporate prior linkage information when such knowledge exists.
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References
Thierens, D., Goldberg, D.E.: Mixing in genetic algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA 1993), pp. 38–45 (1993)
Goldberg, D.E., Deb, K., Thierens, D.: Toward a better understanding of mixing in genetic algorithms. Journal of the Society of Instrument and Control Engineers 32, 10–16 (1993)
Kargupta, H.: The gene expression messy genetic algorithm. In: Proceedings of the, IEEE International Conference on Evolutionary Computation, pp. 814–819 (1996)
Munetomo, M., Goldberg, D.E.: Linkage identification by non-monotonicity detectio for overlapping functions. Evolutionary Computation 7, 377–398 (1999)
Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Tech. Rep. No. CMUCS- 94-163, Carnegie Mellon University, Pittsburgh, PA (1994)
Mühlenbein, H., Mahnig, T.: FDA - a scalable evolutionary algorithm for the optimization for the optimization of additively decomposed functions. Evolutionary Computation 7, 353–376 (1999)
Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The bayesian optimization algorithm. In: Proceedings of Genetic and Evolutionary Computation Conference 1999 (GECCO 1999), pp. 525–532 (1999)
Levenick, J.R.: Metabits: Generic endogenous crossover control. In: Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA 1995), pp. 88–95 (1995)
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)
Chen, Y.-p., Goldberg, D.E.: Convergence time for the linkage learning genetic algorithm. In: Proceedings of the, Congress on Evolutionary Computation (CEC 2004), N/A (2004) (to appear)
Harik, G.R.: Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. PhD thesis, University of Michigan, Ann Arbor, MI (1997)
Chen, Y.-p., Goldberg, D.E.: Introducing start expression genes to the linkage learning genetic algorithm. In: Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature (PPSN VII), pp. 351–360 (2002)
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Dordrecht (2002)
Chen, Y., Goldberg, D.E.: Tightness time for the linkage learning genetic algorithm. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 837–849. Springer, Heidelberg (2003)
Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. Foundations of Genetic Algorithms 2, 93–108 (1993)
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 the 1997 IEEE International Conference on Evolutionary Computation, pp. 7–12 (1997)
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Chen, Yp., Goldberg, D.E. (2004). Introducing Subchromosome Representations to the Linkage Learning Genetic Algorithm. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_97
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DOI: https://doi.org/10.1007/978-3-540-24854-5_97
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