Evolving Neural Networks through Augmenting Topologies-英文文献.pdf

Evolving Neural Networks through Augmenting Topologies Kenneth O. Stanley kstanley@cs.utexas.edu Department of Computer Sciences, The University of Texas at Austin, Austin, TX 78712, USA Risto Miikkulainen risto@cs.utexas.edu Department of Computer Sciences, The University of Texas at Austin, Austin, TX 78712, USA Abstract An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolu- tion 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 differ- ent topologies, (2) protecting structural innovation using speciation, and (3) incremen- tally 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 im- portant 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. 1 Introduction Neuroevolution (NE), the artificial evolution of neural networks using genetic algo- rithms, has shown great promise in complex reinforcement learning tasks (Gomez and Miikkulainen, 1999; Gruau et al., 1996; Moriarty and Miikkulainen, 1997; Po