Using genetic search to exploit the emergent behavior of neural networks
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Biological Cybernetics
Fractally configured neural networks
Neural Networks
Designing application-specific neural networks using the genetic algorithm
Advances in neural information processing systems 2
Automatic definition of modular neural networks
Adaptive Behavior
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
Parametric Connectivity: Training of Constrained Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Full Automatic ANN Design: A Genetic Approach
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
SA-Prop: Optimization of Multilayer Perceptron Parameters Using Simulated Annealing
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
Intelligent systems: architectures and perspectives
Recent advances in intelligent paradigms and applications
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward neural networks has increased. Most of the methods are based on evolutionary computation paradigms. Some of the designed methods are based on direct representations of the parameters of the network. These representations do not allow scalability, so to represent large architectures, very large structures are required. An alternative more interesting are the indirect schemes. They codify a compact representation of the neural network. In this work, an indirect constructive encoding scheme is presented. This scheme is based on cellular automata representations in order to increase the scalability of the method.