Файл:Evolving Neural Networks through Augmenting Topologies Stanley.ec02.pdf
Kenneth O. Stanley email@example.com Department of Computer Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Risto Miikkulainen firstname.lastname@example.org Department of Computer Sciences, The University of Texas at Austin, Austin, TX 78712, USA
An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.
Keywords: Genetic algorithms, neural networks, neuroevolution, network topologies, speciation, competing conventions.
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