Introducing Multi-objective Optimization in Cooperative Coevolution of Neural Networks

Authors:
Nicolás García-Pedrajas;Eloy Sanz-Tapia;Domingo Ortiz-Boyer;César Hervás-Martínez
Affiliations:
-;-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Year:
2001

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Abstract

This paper presents MONet (Multi-Objective coevolutive NETwork), a cooperative coevolutionary model for evolving artificial neural networks that introduces concepts taken from multi-objective optimization. This model is based on the idea of coevolving subnetworks that must cooperate to form a solution for a specific problem, instead of evolving complete networks. The fitness of each member of the subpopulations of subnetworks is evaluated using an evolutionary multi-objective optimization algorithm. This idea has not been used before in the area of evolutionary artificial neural networks. The use of a multiobjective evolutionary algorithm allows the definition of as many objectives as could be interesting for our problem and the optimization of these objectives in a natural way.