Self-organised evolutionary neural networks: algorithms and applications

Authors:
S. D. Likothanassis;E. F. Georgopoulos
Affiliations:
Dept. of Computer Engineering & Informatics, University of Patras, Greece and University of Patras Artificial Intelligence Research Center (U.P.A.I.R.C.), University of Patras, Greece and Computer ...;Dept. of Computer Engineering & Informatics, University of Patras, Greece and University of Patras Artificial Intelligence Research Center (U.P.A.I.R.C.), University of Patras, Greece and Computer ...
Venue:
Highly parallel computaions
Year:
2001

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Abstract

Evolving artificial neural networks (ANN) is a new method that, except for the training, was applied to the structure optimization problem. Several methods have been reported in recent years. Most of these methods are either very complicated or are applied under certain restrictions imposed by the designer. In this work, three new classes of evolutionary algorithms, for self-organized neural networks' training, will be presented. First, a modified genetic algorithm (MGA) is used to both evolve and train a population of multi-layered perceptrons (MLP) neural networks and to find a (near) optimum network architecture. This method is a generalisation of an existing one and is used for the first time for biosignal prediction. A different approach that combines ideas from both the evolution and the adaptive signal processing, considers the neural unit as a non-linear system, with p inputs and one output. Then the localized extended Kalman filter (LEKF) can be used as the training algorithm of such a neuron. First, we create a population of such systems with a random number of inputs. Then we apply the genetic operators to evolve the system's structure, using as a fitness function the inverse of the mean square error (MSE) function. After a small number of iterations the algorithm converges to the near optimum network, by means of the MSE minimization. A generalisation of this method, where the evolution is performed in the hidden region, is presented. Each of the proposed classes of algorithms was tested using artificial as well as real world data.