Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

TL;DR

This paper introduces a hybrid learning approach combining evolutionary algorithms and local search methods to optimize neural networks, achieving faster convergence and improved learning in complex problem spaces.

Contribution

It presents a novel hybrid meta-heuristic method integrating evolutionary and local search techniques for neural network optimization.

Findings

Effective in optimizing neural networks for chaotic time series

Faster convergence compared to direct evolutionary approaches

Competitive performance against neuro-fuzzy systems and global optimization methods

Abstract

Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex solution space. In this paper, we propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1st and 2nd order error information) to improve the learning and faster convergence obtained using a direct evolutionary approach. The proposed technique is tested on three different chaotic time series and the test results are compared with some popular neuro-fuzzy systems and a recently developed cutting angle method of global optimization. Empirical results reveal that the proposed technique is efficient in spite of the computational complexity.