1. 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Introducing “Proteins” into Genetic Algorithms Virginie LEFORT, Carole KNIBBE, Guillaume BESLON, Joël FAVREL INSA-IF/PRISMa, FRANCE Artificial Life and Behaviour Team (ALAB)

2. 2 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Introduction: Origin of species Natural (Darwinian) evolution  Variation of the genotype (  variation of the phenotype)  Extinction of the less fitted individuals  Preservation (and diffusion) of favourable variations  Rejection of unfavourable variations Information support (genotype)  DNA  Genes (DNA coding sequences) Genotype to phenotype mapping (simplified!)  Transcription-translation (genes  proteins)  Biochemistry (proteins  cells)

3. 3 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Principle of genetic algorithms Mimic darwinian evolution in the context of parametric optimization  All parameters are aligned to build a (genetic) sequence  An artificial population is randomly generated  Individuals reproduce themselves (generation loop)  Selection mechanism based on a fitness function  The genetic sequence can be modified during the reproduction process (Mutations, Crossover) Genetic algorithms are very efficient They can be applied to a wide range of problems even when no a priori knowledge is available

4. 4 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Principles of genetic algorithms The reproduction loop : Selection Reproduction Fitness Evaluation

5. 5 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 But... The genotype structure is chosen initially (and arbitrarily) The genotype structure constraints the evolutionary process  Close genes evolve together even though the corresponding parameters are independent  Distant genes evolve separately even though the corresponding parameters are dependent  Building blocks hypothesis (J.H. Holland) The algorithm precision is also chosen initially  Precision depends on the parameter encoding  Fixed along the overall evolutionary process  Precision generally is the same for all parameters

6. 6 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Why ? The genotype to phenotype mapping is too simple  one gene  one parameter  “linear” transformation The algorithm depends on the genetic structure  The genetic structure cannot evolve Gene 1Gene 2Gene 3Gene 4Gene 5 Param 1Param 2Param 3Param 4Param 5

7. 7 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Genetic structure constraints In genetic algorithms the genome is directly mapped into a phenotype The genome structure cannot be modified  Under-specified parameters, Gene 1Gene 2Gene 3Gene 5 Param 1Param 2Param 3???Param 5

8. 8 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 In genetic algorithms the genome is directly mapped into a phenotype The genome structure cannot be modified  Over-specified parameters, Gene 1Gene 2Gene 3Gene 4Gene 5Gene 4’ Param 1Param 2Param 3???Param 5 Genetic structure constraints

9. 9 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 In genetic algorithms the genome is directly mapped into a phenotype The genome structure cannot be modified  Incoherent crossing-over Gene 3’Gene 5’Gene 1’Gene 4’Gene 2’Gene 1Gene 2Gene 3Gene 4Gene 5 ??? Param 4’??? Gene 1Gene 2Gene 1’Gene 4’Gene 2’ Genetic structure constraints

10. 10 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 … in biology ? In living beings, different genetic structures give rise to different organisms on the basis of the same translation mechanism …  Genetic principles of the C. Elegans worm are (quite) the same as for bacterias or humans …  The rules are the same in (quite) all the living kingdom … The gene number, size, position (locus), order … are free to evolve  The information sources are (only) the coding sequences Why do we loose this property in GAs ?

11. 11 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 The proteome In biology there is an intermediate level between the genotype and the phenotype : The genotype structure is lost … Genotype and phenotype structures can evolve separately... Phenotype Gene 1Gene 2Gene 3Gene 4Gene 5 Proteome

12. 12 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 The RBF-Gene algorithm: Basic ideas Back to the “biological” gene definition  The genome is a succession of coding and non-coding sequences  Coding sequences (genes) are identified by their local context  Each gene expresses a protein whose function is “only” determined by the local sequence  The local sequence is translated thanks to a “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” functions

13. 13 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Our “protein” layer The phenotype is an R n  R m function (regression function) The RBF-Gene model introduces an intermediate layer between the parameters and the regression function  The function is a linear combination of elementary kernel functions  The kernel shape is predefined (e.g. gaussian functions, sinus, …)  one coding sequence (one gene)  one kernel (event. not effective)  The genetic code is used to translate the gene sequence into kernel parameters Example: R  R gaussian kernels  Three parameters/kernel : μ i, σ i and w i  The final phenotype is given by : μ σ Kernel K i

14. 14 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 The genetic code Biological genetic code  4 bases (A, C, G, T)  64 codons (3 bases)  4 specific codons : Start (‘ATG’) and Stop (‘TAA’, ‘TAG’ and ‘TGA’)  20 amino-acids RBF-Gene genetic code  Simplification : direct use of the “DNA” bases (n bases)  2 specific bases : Start (‘A’) and Stop (‘B’)  2 bases for each kernel parameter (e.g. ‘C’ and ‘D’ for parameter w )  The number of bases depends on the number of parameters (i.e. on the function dimension)  Binary, variable length Gray code...

15. 15 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 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

16. 16 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 The reproduction loop General Principle: Same as GAs  Biologically inspired operators (local, global, …) Fitness Evaluation Selection Reproduction

17. 17 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Advantages of the RBF-Gene model The regression function is computable whatever the genome structure (size, genes number, genes order, …)  The algorithm is (partly) problem-independant The algorithm adapts the gene number  The algorithm can adapt the phenotype complexity The algorithm adapts the gene length  The algorithm can adapt the phenotype precision  The algorithm can enhance the precision during the evolutionary process The “protein” layer enables us to analyse the phenotype  E.g. One kernel  one fuzzy rule

18. 18 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Example: regression on a “toy-problem” Composition of 5 gaussian functions Gaussian noise :  =0.05 Two example sets : Learning set (50 points) Validation set (50 points) Parameters : Population size : 100 Initial genome size : 200 Number of codons : 8 Mutation rate : 5.10 -4 / base Indel rate : 2 x 5. 10 -4 / base Rearrangement rate : 3 x 0.02 / indiv. Crossing-over rate : 0.6 / indiv. Fitness criteria : mean square error

19. 19 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (1): Evolution of the fitness

20. 20 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (2): Genome, “proteome” and phenotype Generation: 0 Initial population : Genome size : 200 Number of kernels: 16 (4 coding) Learning fitness: 1.3612 Validation fitness: 1.0056 Final results : Genome size : 472 Number of kernels: 15 (10 coding) Learning fitness: 0.0206 Validation fitness: 0.0497 Generation: 2000

21. 21 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (2): “proteome” and phenotype Generation: 2000

22. 22 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (3): Overfitting

23. 23 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (3): Overfitting

24. 24 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (4): Genome size

25. 25 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (5): Number of genes

26. 26 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (6): Gene size (i.e. precision)

27. 27 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Results (7): Coding proportion

28. 28 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Conclusion Reorganization of the genome DURING and BY the evolutionary process  The algorithm adapts the gene number  The algorithm adapts the gene size Tested on the abalone dataset (R 8 to R regression)  Very good results (but slow computations) Perspectives: Evolution of neural networks  The final structure is an RBF-Network …  Other architectures are possible (MLP, recurrent networks, …)  The algorithm adapts the synaptic weights and the network structure (e.g. number of neurons)  Rules extraction from the proteome

29. 29 09/20/04 Introducing Proteins into Genetic Algorithms – CSIMTA'04 Questions ?