Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Automatic definition of modular neural networks
Adaptive Behavior
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Algorithms: Theory and Applications in Signal Processing
Learning Algorithms: Theory and Applications in Signal Processing
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural Networks for Vision, Speech, and Natural Language
Neural Networks for Vision, Speech, and Natural Language
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Simultaneous optimization of weights and structure of an RBF neural network
EA'05 Proceedings of the 7th international conference on Artificial Evolution
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This paper describes a new scheme of binary codification of artificial neural networks designed to generate automatically neural networks using any optimization method. Instead of using direct mapping of strings of bits in network connectivities, this particular codification abstracts binary encoding so that it does not reference the artificial indexing of network nodes; this codification employs shorter string length and avoids illegal points in the search space, but does not exclude any legal neural network. With these goals in mind, an Abelian semi-group structure with neutral element is obtained in the set of artificial neural networks with a particular internal operation called superimposition that allows building complex neural nets from minimum useful structures. This scheme preserves the significant feature that similar neural networks only differ in one bit, which is desirable when using search algorithms. Experimental results using this codification with genetic algorithms are reported and compared to other codification methods in terms of speed of convergence and the size of the networks obtained as a solution.