![]() ![]() This is in contrast to indirect encoding schemes which define rules that allow the network to be constructed without explicitly representing every connection and neuron allowing for more compact representation. In order to encode the network into a phenotype for the GA, NEAT uses a direct encoding scheme which means every connection and neuron is explicitly represented. NEAT is an example of a topology and weight evolving artificial neural network (TWEANN) which attempts to simultaneously learn weight values and an appropriate topology for a neural network. This yields a situation whereby a trial and error process may be necessary in order to determine an appropriate topology. Traditionally a neural network topology is chosen by a human experimenter, and effective connection weight values are learned through a training procedure. On simple control tasks, the NEAT algorithm often arrives at effective networks more quickly than other contemporary neuro-evolutionary techniques and reinforcement learning methods.
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