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Designing a well-generalized architecture for artificial neural networks (ANNs) is an important task. This paper presents an adaptive memetic algorithm with a rank-based mutation, denoted as AMARM, to design ANN architectures. The proposed algorithm introduces an adaptive multi-local search mechanism to simultaneously fine-tune the number of hidden neurons and connection weights. The adaptation of the multi-local search mechanism is achieved by identifying effective local searches based on their search characteristics. Such an algorithm is distinguishable from previous evolutionary algorithm-based methods that incorporate one single local search for evolving ANN architectures. Furthermore, a rank-based mutation strategy is devised for avoiding premature convergence during evolution. The performance of the proposed algorithm has been evaluated on a number of benchmark problems and compared with related work. The results show that the AMARM can be used to design compact ANN architectures with good generalization capability, outperforming related work.

An Adaptive Memetic Algorithm With Rank-Based Mutation for Artificial Neural Network Architecture Optimization