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Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. [1] It is most commonly applied in artificial life , general game playing [ 2 ] and evolutionary robotics .
Evolutionary computation from computer science is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms.
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002). Kenneth O. Stanley; Bobby D. Bryant & Risto Miikkulainen (2003). "Evolving Adaptive Neural Networks with and without Adaptive Synapses" (PDF). Proceedings of the 2003 IEEE Congress on Evolutionary Computation (CEC-2003). Colin Green (2004).
They belong to the class of metaheuristics and are a subset of population based bio-inspired algorithms [1] and evolutionary computation, which itself are part of the field of computational intelligence. [2] The mechanisms of biological evolution that an EA mainly imitates are reproduction, mutation, recombination and selection.
Natural computing, [1] [2] also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials (e.g., molecules) to compute.
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover. [ 1 ] [ 2 ] Evolutionary programming differs from evolution strategy ES( μ + λ {\displaystyle \mu +\lambda } ) in one detail. [ 1 ]
Reservoir computing is a computation framework that may be viewed as an extension of neural networks. [58] Typically an input signal is fed into a fixed (random) dynamical system called a reservoir whose dynamics map the input to a higher dimension.
Evolutionary acquisition of neural topologies (EANT/EANT2) is an evolutionary reinforcement learning method that evolves both the topology and weights of artificial neural networks. It is closely related to the works of Angeline et al. [ 1 ] and Stanley and Miikkulainen. [ 2 ]