A Memetic Pareto Evolutionary Approach to Artificial Neural Networks

Evolutionary Artificial Neural Networks (EANN) have been a focus of research in the areas of Evolutionary Algorithms (EA) and Artificial Neural Networks (ANN) for the last decade. In this paper, we present an EANN approach based on pareto multi-objective optimization and differential evolution augmented with local search. We call the approach Memetic Pareto Artificial Neural Networks (MPANN). We show empirically that MPANN is capable to overcome the slow training of traditional EANN with equivalent or better generalization.

• Type: Conference
• Year: 2001
• Where: AUSAI
• Authors: Hussein A. Abbass
• Keywords: Approach Memetic Pareto, Artificial Intelligence, Artificial Neural Networks, AUSAI 2001, Pareto Multi-objective Optimization