Continuous probabilistic model building genetic network programming using reinforcement learning

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@Article{Li:2015:ASC,

• author = "Xianneng Li and Kotaro Hirasawa",
• title = "Continuous probabilistic model building genetic network programming using reinforcement learning",
• journal = "Applied Soft Computing",
• year = "2015",
• volume = "27",
• number = "Supplement C",
• pages = "457--467",
• keywords = "genetic algorithms, genetic programming, genetic network programming, Estimation of distribution algorithm, Probabilistic model building, Continuous optimization, Reinforcement learning",
• ISSN = "1568-4946",
• URL = "http://www.sciencedirect.com/science/article/pii/S156849461400533X",
• DOI = "doi:10.1016/j.asoc.2014.10.023",
• abstract = "Recently, a novel probabilistic model-building evolutionary algorithm (so called estimation of distribution algorithm, or EDA), named probabilistic model building genetic network programming (PMBGNP), has been proposed. PMBGNP uses graph structures for its individual representation, which shows higher expression ability than the classical EDAs. Hence, it extends EDAs to solve a range of problems, such as data mining and agent control. This paper is dedicated to propose a continuous version of PMBGNP for continuous optimization in agent control problems. Different from the other continuous EDAs, the proposed algorithm evolves the continuous variables by reinforcement learning (RL). We compare the performance with several state-of-the-art algorithms on a real mobile robot control problem. The results show that the proposed algorithm outperforms the others with statistically significant differences.",

}

Genetic Programming entries for Xianneng Li Kotaro Hirasawa

Citations