6/22/04 The RBF-Gene Model – GECCO'04

The RBF-Gene ModelA 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, 2004

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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, 2004

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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, 2004

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G2 G3 G4

The genotype to phenotype mapping

G1

FE…BEFDGGCFDGHEGA…D

μ

σKernel K1:

σ: 00010(gray) 00010(bin) 0.0625

Phenotype :

n

iii Kw

1

1σH

0σG

1μF

0μE

1wD

0wC

StopB

StartA

ValueParameterBase

Genetic code

w: 101(gray) 110(bin) 0.75μ: 0110(gray) 0100(bin) 0.25

The RBF-Gene Model – GECCO'0422 June, 2004

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The reproduction loop Biologically inspired operators :

Evaluation

Selection

Reproduction

The RBF-Gene Model – GECCO'0422 June, 2004

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Results on a toy problem (RR)Generation: 0

Final results :• Number of kernels: 10• Learning fitness: 0.0206• Validation fitness: 0.0497

Generation: 2000

The RBF-Gene Model – GECCO'0422 June, 2004

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Conclusion

Reorganization of the genome DURING and BY the evolutionary process

Validated on the abalone dataset (R8R 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)

The RBF-Gene Model – GECCO'0422 June, 2004

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Questions ?