Molecular Self-Organization in the Development Model for the Evolution of Large-scale Artificial Neural Networks
نویسندگان
چکیده
We argue that molecular self-organisation during embryonic development allows evolution to perform highly nonlinear combinatorial optimisation. A structured approach to architectural optimisation of large-scale Artificial Neural Networks using this principle is presented. We also present simulation results demonstrating the evolution of an edge detecting retina using the proposed methodology.
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تاریخ انتشار 1998