Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
A new trust region technique for the maximum weight clique problem
Discrete Applied Mathematics - Special issue: International symposium on combinatorial optimization CO'02
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
A novel similarity-based crossover for artificial neural network evolution
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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This paper presents a new evolutionary system using genetic algorithm for evolving artificial neural networks (ANNs). Existing genetic algorithms (GAs) for evolving ANNs suffer from the permutation problem. Frequent and abrupt recombination in GAs also have very detrimental effect on the quality of offspring. On the other hand, Evolutionary Programming (EP) does not use recombination operator entirely. Proposed algorithm introduces a recombination operator using graph matching technique to adapt structure of ANNs dynamically and to avoid permutation problem. The complete algorithm is designed to avoid frequent recombination and reduce behavioral disruption between parents and offspring. The evolutionary system is implemented and applied to three medical diagnosis problems - breast cancer, diabetes and thyroid. The experimental results show that the system can dynamically evolve compact structures of ANNs, showing competitiveness in performance.