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- W2119971563 abstract "The author shows that the generic algorithm (GA) can be applied successfully to training nonconvergent networks, and presents some examples of their extraordinary behavioral versatility. He first gives a brief summary of the GA and the genetic programming of neural networks. He shows how GP techniques were used to evolve GenNets with specified operating conditions, and demonstrates some of the extraordinary capacities of time-dependent GenNets. He also makes a plea to the neural network research community to 'shift its sights upwards' by devoting more effort to thinking about 'dynamic' neural networks in general, and the theory of GenNet dynamics and 'evolvability' in particular. >" @default.
- W2119971563 created "2016-06-24" @default.
- W2119971563 creator A5058202066 @default.
- W2119971563 date "2002-12-09" @default.
- W2119971563 modified "2023-09-25" @default.
- W2119971563 title "GenNets: genetically programmed neural nets-using the genetic algorithm to train neural nets whose inputs and/or outputs vary in time" @default.
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- W2119971563 doi "https://doi.org/10.1109/ijcnn.1991.170594" @default.
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