Abstract
This paper describes the GAuGE system, Genetic Algorithms using Grammatical Evolution, which uses Grammatical Evolution to perform as a position independent Genetic Algorithm. Gauge has already been successfully applied to domains such as bit level, sorting and regression problems, and our experience suggests that it evolves individuals with a similar dynamic to Genetic Programming. That is, there is a hierarchy of dependency within the individual, and, as evolution progresses, those parts at the top of the hierarchy become fixed across a population. We look at the manner in which the population evolves the representation at the same time as optimising the problem, and demonstrate there is a definite emergence of representation.
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References
Keijzer M., Ryan C, O’Neill M., Cattolico M. and Babovic V. (2001). Ripple Crossover in Genetic Programming. In Proceedings of the Fourth European Conference on Genetic Programming, J. Miller et al., (Eds. ), pp. 74–86. Springer Lecture Notes in Computer Science, Vol. 2038. Springer-Verlag.
McPhee N. F. and Hopper N. J. (1999). Analysis of Genetic Diversity through Population History. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando, Florida, pp. 1112–1120. Morgan Kaufmann.
Nicolau, M. and Ryan, C. (2002). LINKGAUGE: Tackling hard deceptive problems with a new linkage learning genetic algorithm. In Proceedings of the Genetic and Evolutionary Compu-tation Conference (GECCO 2002), W. Langdon et al. (Eds. ), pp 488–494. Morgan Kaufmann Publishers, San Francisco.
O’Neill, M. (2001). Automatic Programming in an Arbitrary Language: Evolving Programs with Grammatical Evolution. Doctoral Dissertation, University of Limerick.
Rudnick M. Genetic Algorithms and Fitness Variance with an Application to the Automated Design of Articial Neural Networks. Unpublished Doctoral Dissertation, Oregon Graduate Institute of Science and Technology (1992).
Ryan, C, Collins, J. J. and O’Neill, M. (1998). Grammatical Evolution: Evolving Programs for an Arbitrary Language. In Proceedings of the First European Workshop on Genetic Program-ming, EuroGP’98, Banzhaf et al. . (Eds. ). Lecture Notes in Computer Science (LCNS) Vol. 1391, pp. 83–95. Springer-Verlag.
Ryan, C. Nicolau, M. and O’Neill, M. (2002). Genetic Algorithms using Grammatical Evolution. In Proceedings of EuroGP-2002, J. Foster et al. (Eds. ). Lecture Notes in Computer Science, Vol. 2278, pp 278–287. Springer-Verlag.
Thierens, D., Goldberg, D. E. and Pereira, A. G. (1998). Domino convergence, drift, and the temporal-salience structure of problems. In Proceedings of the 1998 IEEE World Congress on Computational Intelligence, pp 535–540.
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Ryan, C., Nicolau, M. (2003). Doing Genetic Algorithms the Genetic Programming Way. In: Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice. Genetic Programming Series, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8983-3_12
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DOI: https://doi.org/10.1007/978-1-4419-8983-3_12
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