Applying neural pruning to NEAT
نویسنده
چکیده
Open-ended machine learning problems require various level of flexibility from learning agents. Stanley (2004) presented in his paper that incremental complexification in neural network structure usually results in more effective learning process, while subsequent researches by James and Tucker showed simplifying the structures often results in similarly good solution. In this paper, we test various methods of allowing more flexibility in evolving neural network structures and demonstrate it is not always optimal to allow one-way complexifying evolution from the most simple structure.
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