A Novel Estimation of Distribution Algorithm Using Graph-based Chromosome Representation and Reinforcement Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Li:2011:ANEoDAUGCRaRL,

• title = "A Novel Estimation of Distribution Algorithm Using Graph-based Chromosome Representation and Reinforcement Learning",
• author = "Xianneng Li and Bing Li and Shingo Mabu and Kotaro Hirasawa",
• pages = "37--44",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, EDA, estimation of distribution algorithms, probabilistic model building genetic network programming",
• DOI = "doi:10.1109/CEC.2011.5949595",
• abstract = "This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for Xianneng Li Bing Li Shingo Mabu Kotaro Hirasawa

Citations