Kernel-based online NEAT for keepaway soccer

Authors:
Yun Zhao;Hua Cai;Qingwei Chen;Weili Hu
Affiliations:
Department of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China;Department of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China;Department of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China;Department of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
Venue:
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Year:
2007

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Abstract

This paper presents a kernel-based online neuroevolutionary of augmenting topology (KO-NEAT) algorithm, which borrowing the selection mechanisms used in temporal difference (TD) algorithms and combining the kernel function approximator for individual fitness initiation. KO-NEAT can improve evolution's online performance of NEAT and learns more quickly. Empirical results in keepaway soccer problem demonstrate that KO-NEAT can substantially improve the original algorithm.