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How Functional Dependency Adapts to Salience Hierarchy in the GAuGE System

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Genetic Programming (EuroGP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2610))

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Abstract

GAuGE is a position independent genetic algorithm that suffers from neither under nor over-specification, and uses a genotype to phenotype mapping process. By specifying both the position and the value of each gene, it has the potential to group important data together in the genotype string, to prevent it from being broken up and disrupted during the evolution process. To test this ability, GAuGE was applied to a set of problems with exponentially scaled salience. The results obtained demonstrate that GAuGE is indeed moving the more salient genes to the start of the genotype strings, creating robust individuals that are built in a progressive fashion from the left to the right side of the genotype.

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References

  1. Banzhaf, W.: Genotype-Phenotype-Mapping and Neutral Variation-A case study in Genetic Programming. In: Davidor et al., (eds.): Proceedings of the third conference on Parallel Problem Solving from Nature. Lecture Notes in Computer Science, Vol. 866. Springer-Verlag. (1994) 322–332

    Google Scholar 

  2. Bean, J.: Genetic Algorithms and Random Keys for Sequencing and Optimization. ORSA Journal on Computing, Vol. 6, No. 2. (1994) 154–160

    MATH  Google Scholar 

  3. Harik, G.: Learning Gene Linkage to Efficiently Solve Problems of Bounded Difficulty Using Genetic Algorithms. Doctoral Dissertation, University of Illinois (1997)

    Google Scholar 

  4. Keijzer M., Ryan C., O’Neill M., Cattolico M., and Babovic V.: Ripple Crossover in Genetic Programming. In: Miller et al., (eds.): Proceedings of the Fourth European Conference on Genetic Programming. Springer. (pp. 74–86) Lecture Notes in Computer Science, Vol. 2038. Springer-Verlag. (2001) 74-86

    Google Scholar 

  5. Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge University Press. (1983)

    Google Scholar 

  6. Lobo, F., Goldberg, D. E., and Pelikan, M.: Time complexity of genetic algorithms on exponentially scaled problems. In: Whitley et al., (eds.): Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2000. Morgan Kaufmann Publishers, San Francisco (2000) 151–158

    Google Scholar 

  7. Nicolau, M., and Ryan, C.: LINKGAUGE: Tackling hard deceptive problems with a new linkage learning genetic algorithm. In: Langdon et al., (eds.): Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2002. Morgan Kaufmann Publishers, San Francisco (2002) 488–494

    Google Scholar 

  8. O’Neill, M.: Automatic Programming in an Arbitrary Language: Evolving Programs with Grammatical Evolution. Doctoral Dissertation, University of Limerick (2001)

    Google Scholar 

  9. O’Neill, M., and Ryan, C.: Grammatical Evolution. IEEE Transactions on Evolutionary Computation, Vol. 5, No. 4. (2001) 349–358

    Article  Google Scholar 

  10. O’Neill, M., and Ryan, C.: Genetic Code Degeneracy: Implications for Grammatical Evolution and Beyond. In: Floreano et al., (eds.): Proceedings of the Fifth European Conference on Artificial Life, ECAL’99. Lecture Notes in Computer Science, Vol. 1674. Springer-Verlag. (1999)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Ryan, C., Collins, J.J., and O’Neill, M.: Grammatical Evolution: Evolving Programs for an Arbitrary Language. In: Banzhaf et al., (eds.): Proceedings of the First European Workshop on Genetic Programming, EuroGP’98. Lecture Notes in Computer Science, Vol. 1391. Springer-Verlag. (1998) 83–95

    Google Scholar 

  13. Ryan, C., Nicolau, M., and O’Neill, M.: Genetic Algorithms using Grammatical Evolution. In: Foster et al, (eds.): Proceedings of EuroGP-2002. Lecture Notes in Computer Science, Vol. 2278. Springer-Verlag. (2002) 278–287

    Google Scholar 

  14. Stringer, H., and Wu, A. S.: A Simple Method for Detecting Domino Convergence and Identifying Salient Genes Within a Genetic Algorithm. In: Langdon et al., (eds.): Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2002. Morgan Kaufmann Publishers, San Francisco (2002) 594–601

    Google Scholar 

  15. Thierens, D., Goldberg, D. E., and Pereira, A.G.: Domino convergence, drift, and the temporal-salience structure of problems. In: Proceedings of the 1998 IEEE World Congress on Computational Intelligence. (1998) 535–540

    Google Scholar 

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Nicolau, M., Ryan, C. (2003). How Functional Dependency Adapts to Salience Hierarchy in the GAuGE System. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_14

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  • DOI: https://doi.org/10.1007/3-540-36599-0_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00971-9

  • Online ISBN: 978-3-540-36599-0

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