Optimization of evolutionary neural networks using hybrid learning algorithms

TLDR: In this paper, a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods is proposed to improve the learning and faster convergence obtained using a direct evolutionary approach, which is tested on three different chaotic time series and the test results are compared with some popular neuro-fuzzy systems and a cutting angle method of global optimization.

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. We propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1/sup st/ and 2/sup nd/ 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 cutting angle method of global optimization. Empirical results reveal that the proposed technique is efficient in spite of the computational complexity.