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A Bio-inspired Genetic Algorithm with a Self-Organizing Genome: The RBF-Gene Model

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Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

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Abstract

Although Genetic Algorithms (GAs) are directly inspired by Darwinian principles, they use an over-simplistic model of chromosome and genotype to phenotype mapping. This simplification leads to a lack of performance, mainly because the chromosome structure directly constrains the evolution process.

In biology, the structure of the chromosome is free to evolve. The main feature permitting it is the presence of an intermediate layer (the proteins) between genotype and phenotype: Whatever the size and the locus of a gene, it is translated into a protein and all the proteins are combined to “produce” the phenotype.

Some authors, like Goldberg [1], have tried to introduce some independence between the genotype and the phenotype in GAs but none have really introduced the “protein level”. Thus, they do not really part the two levels. We propose a new model of GA introducing such an intermediate level in order to permit evolvability during and by the evolutionary process to improve convergence.

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References

  1. Goldberg, D.E., Deb, K., Kargupta, H., Harik, G.: Rapid accurate optimization of difficult problems using fast messy genetic algorithms. In: Forrest, S. (ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, CA, pp. 56–64. Morgan Kaufmann, San Francisco (1993)

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  2. UCI Machine Learning Website, http://www.ics.uci.edu/~mlearn/MLRepository.html Abalone data set (consulted in 2003)

  3. Automatic Knowledge Miner (AKM) Server: Data mining analysis (request abalone). Technical report, AKM (WEKA), University of Waikato, Hamilton, New Zealand (2003)

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© 2004 Springer-Verlag Berlin Heidelberg

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Lefort, V., Knibbe, C., Beslon, G., Favrel, J. (2004). A Bio-inspired Genetic Algorithm with a Self-Organizing Genome: The RBF-Gene Model. 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_46

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  • DOI: https://doi.org/10.1007/978-3-540-24855-2_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22343-6

  • Online ISBN: 978-3-540-24855-2

  • eBook Packages: Springer Book Archive

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