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6/22/04 The RBF-Gene Model – GECCO'04 The RBF-Gene Model A bio-inspired genetic algorithm with a self-organizing genome Virginie LEFORT, Carole KNIBBE, Guillaume BESLON, Joël FAVREL INSA-IF/PRISMa, FRANCE
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The RBF-Gene Model – GECCO'0422 June, 2004 2 Basic ideas Back to the “biological” gene definition A gene is a coding sequence in an ocean of non-coding sequences Each gene express a protein which function is “only” determined by the local sequence (genetic code) Proteins interact to produce the phenotype The RBF-Gene model is based on: A “protein layer” between genotype and phenotype A “genetic” code to find the genes and the associated protein function The phenotype is computable whatever the genome structure
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The RBF-Gene Model – GECCO'0422 June, 2004 3 Application to a regression task Approximation thanks to a parametric regression function The RBF-Gene model introduces an intermediate layer between the parameters and the regression function The regression function is a combination of elementary kernel functions Each coding sequence is translated into a kernel A genetic code is used to compute the kernel parameters from the gene sequence Advantages: The regression function is computable whatever the genome (size, genes number, genes order, …) The algorithm can choose the function complexity (kernel number) The algorithm can choose the kernels precision (gene size)
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The RBF-Gene Model – GECCO'0422 June, 2004 4 G2G3G4 The genotype to phenotype mapping G1 FE…BEFDGGCFDGHEGA…D μ σ Kernel K 1 : σ: 00010 (gray) 00010 (bin) 0.0625 Phenotype : 1σH 0σG 1μF 0μE 1wD 0wC StopB StartA ValueParameterBase Genetic code w: 101 (gray) 110 (bin) 0.75 μ: 0110 (gray) 0100 (bin) 0.25
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The RBF-Gene Model – GECCO'0422 June, 2004 5 The reproduction loop Biologically inspired operators : Evaluation Selection Reproduction
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The RBF-Gene Model – GECCO'0422 June, 2004 6 Results on a toy problem ( R R ) Generation: 0 Final results : Number of kernels: 10 Learning fitness: 0.0206 Validation fitness: 0.0497 Generation: 2000
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The RBF-Gene Model – GECCO'0422 June, 2004 7 Conclusion Reorganization of the genome DURING and BY the evolutionary process Validated on the abalone dataset (R 8 R function) [1,2] Future work: Study of the influence of each parameter Clustering ? [1] UCI Machine Learning Website: Abalone data set (consulted in 2003) (http://www.ics.uci.edu/mlearn/MLRepository.html) [2] 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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The RBF-Gene Model – GECCO'0422 June, 2004 8 Questions ?
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