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- Evolving complex neural networks that age
- Podlena, John R.; Hendtlass, Tim
- The combination of the broad problem searching capabilities of a genetic algorithm with the local maxima location capabilities of a hill climbing algorithm can be a powerful technique for solving classification problems. Producing a number of specialist artificial neural networks, each an expert on one category, can be beneficial when solving problems in which the categories are distinct. This paper describes combining genetic algorithms, hill climbing and sets of specialist artificial neural networks to solve a difficult character recognition problem. It also describes a method by which the effects of a large 'elite' sub-population can be counter-balanced by using an aging coefficient in the fitness calculation.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology. School of Biophysical Sciences and Electrical Engineering
- Proceedings of the IEEE International Conference on Neural Networks (ICNN-95), Perth, Western Australia, Australia, 27 November-01 December 1995, Vol. 2, pp. 590-595
- Publication year
- Ageing coefficient; Ageing neural networks; Calculations; Character recognition; Complex neural network evolution; Computational complexity; Electric network topology; Elite subpopulation; Fitness calculation; Genetic algorithms; Hill climbing algorithm; Local maxima location capabilities; Neural nets; Neural networks; Pattern classification; Problem-searching capabilities; Problem solving; Specialist artificial neural networks; Weight crossover
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