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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 ?
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