Abstract
This paper studies the inducement of sequentiality in genetic algorithms (GAs) for uniformly-scaled problems. Sequentiality is a phenomenon in which sub-solutions converge sequentially in time in contrast to uniform convergence observed for uniformly-scaled problems. This study uses three different grammatical genetic codes to induce sequentiality. Genotypic genes in the grammatical codes are interpreted as phenotypes according to the grammar, and the grammar induces sequential interactions among phenotypic genes. The experimental results show that the grammatical codes can indeed induce sequentiality, but the GAs using them need exponential population sizes for a reliable search.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Thierens, D., Goldberg, D.E.: Mixing in genetic algorithms. In: Proceedings of the 5th International Conference on Genetic Algorithms (ICGA 1993), pp. 38–45 (1993)
Goldberg, D.E.: The race, the hurdle, and the sweet spot: Lessons from genetic algorithms for the automation of design innovation and creativity. Evolutionary Design by Computers, 105–118 (1999)
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell (2002)
Harik, G.R., Goldberg, D.E.: Learning linkage. Foundations of Genetic Algorithms 4, 247–262 (1996)
Harik, G.R.: Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. PhD thesis, University of Michigan, Ann Arbor (1997) , Also IlliGAL Report No. 97005.
Chen, Y.P., Goldberg, D.E.: Introducing start expression genes to the linkage learning genetic algorithm. In: Proceedings of Parallel Problem Solving from Nature , vol. VII, pp. 351–360 (2002)
Chen, Y.P., Goldberg, D.E.: Convergence time for the linkage learning genetic algorithm. IlliGAL Report No. 2003025, Illinois Genetic Algorithms Lab., Univ. of Illinois, Urbana, IL (2003)
Ryan, C., Nicolau, M., O’Neill, M.: Genetic algorithms using grammatical evolution. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 278–287. Springer, Heidelberg (2002)
Nicolau, M., Ryan, C.: How functional dependency adapts to salience hierarchy in the GAuGE system. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 153–163. Springer, Heidelberg (2003)
Ryan, C., Collins, J., O’Neill, M.: Grammatical evolution: Evolving programs for an arbitrary language. In: Proceedings of the First European Conference on Genetic Programming, pp. 83–96 (1998)
O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Transactions on Evolutionary Computation 5, 349–358 (2001)
Rothlauf, F., Goldberg, D.E.: Representations for Genetic and Evolutionary Algorithms. Physica-Verg, Heidelberg (2002)
Whitley, D., Rana, S., Heckendorn, R.: Representation issues in neighborhood search and evolutionary algorithms. In: Genetic Algorithms and Evolution Strategy in Engineering and Computer Science, pp. 39–58. John Wiley & Sons Ltd, West Sussex (1997)
Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3, 493–530 (1989)
Anderson, P.G.: Ordered greed. In: Proceedings of Third International ICSC Symposium on Soft Computing (1999)
Anderson, P.G.: Ordered greed, ii: Graph coloring. In: Proceedings of the Internatinal NAISO Congress on Information science innovations, ISI 2001 (2001)
Kitano, H.: Designing neural networks using genetic algorithms with graph generation system. Complex Systems 4, 461–476 (1990)
Goldberg, D.E., Lingle, J. R.: Alleles, loci, and the traveling salesman problem. In: Proceedings of an International Conference on Genetic Algorithms and Their Applications, pp. 154–159 (1985)
Nicolau, M., Ryan, C.: LINKGAUGE: Tackling hard deceptive problems with a new linkage learning genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference 2002 (GECCO 2002), pp. 488–494 (2002)
Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. Foundations of Genetic Algorithms 2, 93–108 (1993)
Rothlauf, F.: Towards a Theory of Representations for Genetic and Evolutionary Algorithms— Development of Basic Concepts and their Application to Binary and Tree Representations. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana, IL (2001)
Satoh, H., Yamamura, M., Kobayashi, S.: Minimal generation gap model for GAs considering both exploration and expolation. In: Proceedings of the International Conference on Fuzzy Systems, Neural Networks and Soft Computing (Iizuka 1996), pp. 494–497 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ohnishi, K., Sastry, K., Chen, YP., Goldberg, D.E. (2004). Inducing Sequentiality Using Grammatical Genetic Codes. 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_135
Download citation
DOI: https://doi.org/10.1007/978-3-540-24854-5_135
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22344-3
Online ISBN: 978-3-540-24854-5
eBook Packages: Springer Book Archive