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

The RBF-Gene Model – GECCO'0422 June, 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

The RBF-Gene Model – GECCO'0422 June, 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)

The RBF-Gene Model – GECCO'0422 June, G2G3G4 The genotype to phenotype mapping G1 FE…BEFDGGCFDGHEGA…D μ σ Kernel K 1 : σ: (gray)  (bin)  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

The RBF-Gene Model – GECCO'0422 June, The reproduction loop Biologically inspired operators : Evaluation Selection Reproduction

The RBF-Gene Model – GECCO'0422 June, Results on a toy problem ( R  R ) Generation: 0 Final results : Number of kernels: 10 Learning fitness: Validation fitness: Generation: 2000

The RBF-Gene Model – GECCO'0422 June, 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) ( [2] Automatic Knowledge Miner (AKM) Server: Data mining analysis (request abalone). Technical report, AKM (WEKA), University of Waikato, Hamilton, New Zealand (2003)

The RBF-Gene Model – GECCO'0422 June, Questions ?