Evolving both the Topology and Weights of Neural Networks*
نویسندگان
چکیده
Evolutionary algorithms(EAs) have been applied to designing and training artiicial neural net-works(ANNs), independently or in various combinations with other algorithms like back propagation and simulated annealing, which can be distinguished into the evolution of connection weights, of architectures and of learning rules. In this paper, we present an evolutionary approach to designing neural networks both for their topology and connection weights. For we directly represent a candidate network as a graph with weighted edges and vertices, the genetic operators can be designed to be quite natural and eeective. For the mutation operator, we introduced the temperature of an individual on which the mutation based can protect tter individuals while enlarge the search neighborhood of unnt ones. Although we do not use an other ne-tuning procedure to train the connection weights of the networks in each generation, the experiments demonstrate that the algorithm can design an optimal network for some problems successfully and eeciently.
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عنوان ژورنال:
- Parallel Algorithms Appl.
دوره 9 شماره
صفحات -
تاریخ انتشار 1996