Neuroevolution is a method for modifying neural network weights, topologies, or ensembles in order to learn a specific task. Evolutionary computation is used to search for network parameters that maximize a fitness function that measures performance in the task. Compared to other neural network learning methods, neuroevolution is highly general, allowing learning without explicit targets, with nondifferentiable activation functions, and with recurrent networks. It can also be combined with standard neural network learning to, e.g., model biological adaptation. Neuroevolution can also be seen as a policy search method for reinforcement learning problems, where it is well suited to continuous domains and to domains where the state is only partially observable.
Miikkulainen, R. (2014). Neuroevolution. In Encyclopedia of Machine Learning and Data Mining (pp. 1–7). Springer US. https://doi.org/10.1007/978-1-4899-7502-7_594-1
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