Hybrid Heuristics for Optimal Design of Artificial Neural Networks
Ajith Abraham, Baikunth Nath · 2001
Designing the architecture and correct parameters for the learning algorithm is a tedious task for modeling an optimal Artificial Neural Network (ANN), which is smaller, faster and with a better generalization performance. In this paper we explain how a hybrid algorithm integrating Genetic algorithm (GA), Simulated Annealing (SA) and other heuristic procedures can be applied for the optimal design of an ANN. This paper is more concerned with the understanding of current theoretical developments of Evolutionary Artificial Neural Networks (EANNs) using GAs and how the proposed hybrid heuristic procedures can be combined to produce an optimal ANN. The proposed meta-heuristic can be regarded as a general framework for adaptive systems, that is, systems that can change their connection weights, architectures and learning rules according to different environments without human intervention.