Promoter based genetic algorithm
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The Promoter Based Genetic Algorithm (PBGA) is a genetic algorithm for neuroevolution developed by F. Bellas and R.J. Duro at the University of Coruña, in Spain. It evolves variable size feedforward artificial neural networks (ANN) that are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines if that particular unit will be expressed or not.
PBGA basics
The basic unit in the PBGA is a neuron with all of its inbound connections as represented in previous figure.
The genotype of a basic unit is a set of real valued weights followed by the parameters of the neuron and proceeded by an integer valued field that determines the promoter gene value and, consequently, the expression of the unit. By concatenating units of this type we can construct the whole network.
With this encoding it is imposed that the information that is not expressed is still carried by the genotype in evolution but it is shielded from direct selective pressure, maintaining this way the diversity in the population, which has been a design premise for this algorithm. Therefore, a clear difference is established between the search space and the solution space, permitting information learned and encoded into the genotypic representation to be preserved by disabling promoter genes.
PBGA outperforms other neuroevolutionary algorithms in non-stationary problems as shown in [1]
References
[1] F. Bellas, J.A. Becerra, R. J. Duro, Using Promoters and Functional Introns in Genetic Algorithms for Neuroevolutionary Learning in Non-Stationary Problems, Neurocomputing 72, 2134-2145 (2009)
[2] F. Bellas, R. J. Duro, Modelling the world with statiscally neutral PBGAs. Enhancement and real applications, Proc. 9th Internacional Conference on Neural Information Processing (2002)
[3] F. Bellas, R. J. Duro, Statistically neutral promoter based GA for evolution with dynamic fitness functions, Proc. of IASTED International Conference Artificial Intelligence and Applications (2002)