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A Constraint Handling Technique for Implementing Multi-Objective Evolutionary Neural Networks

Pareto-based multi-objective algorithms have been adopted for the design of artificial neural networks and they have attracted much interest, with promising results. Such approaches are usually implemented by means of error minimization while controlling the complexity of the model. This paper proposes a novel methodology for implementing Pareto-based multi-objective optimization within evolutionary neural networks. The originality of the proposed methodology lie in the use of a new constraint handling technique based on a self-adaptive penalty method in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Experimental results show that the proposed approach outperforms other existing evolutionary neural networks approaches reported recently in the literature using the widely accepted Wisconsin breast cancer diagnosis (WBCD) database. The developed approach was also used to simulate chemical unit process and the outcomes of the results are quite encouraging.