Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
Artificial Life
Artificial life approach for continuous optimisation of non-stationary dynamical systems
Integrated Computer-Aided Engineering
Evolving feed-forward neural networks through evolutionary mutation parameters
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Complex networks like the scale-free model proposed by Barabasi-Albert are observed in many biological systems and the application of this topology to artificial neural network leads to interesting considerations. In this paper, we present a preliminary study on how to evolve neural networks with complex topologies. This approach is utilized in the problem of modeling a chemical process with the presence of unknown inputs (disturbance). The evolutionary algorithm we use considers an initial population of individuals with differents scale-free networks in the genotype and at the end of the algorithm we observe and analyze the topology of networks with the best performances. Experimentation on modeling a complex chemical process shows that performances of networks with complex topology are similar to the feed-forward ones but the analysis of the topology of the most performing networks leads to the conclusion that the distribution of input node information affects the network performance (modeling capability).