A Novel Graph-Based Estimation of the Distribution Algorithm and its Extension Using Reinforcement Learning

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@Article{Li:2014:ieeeTEC,

• author = "Xianneng Li and Shingo Mabu and Kotaro Hirasawa",
• title = "A Novel Graph-Based Estimation of the Distribution Algorithm and its Extension Using Reinforcement Learning",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2014",
• volume = "18",
• number = "1",
• pages = "98--113",
• month = feb,
• keywords = "genetic algorithms, genetic programming, genetic network programming, Agent control, estimation of distribution algorithm (EDA), GNP, graph structure, reinforcement learning (RL)",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2013.2238240",
• size = "16 pages",
• abstract = "In recent years, numerous studies have drawn the success of estimation of distribution algorithms (EDAs) to avoid the frequent breakage of building blocks of the conventional stochastic genetic operators-based evolutionary algorithms (EAs). In this paper, a novel graph-based EDA called probabilistic model building genetic network programming (PMBGNP) is proposed. Using the distinguished graph (network) structure of a graph-based EA called genetic network programming (GNP), PMBGNP ensures higher expression ability than the conventional EDAs to solve some specific problems. Furthermore, an extended algorithm called reinforced PMBGNP is proposed to combine PMBGNP and reinforcement learning to enhance the performance in terms of fitness values, search speed, and reliability. The proposed algorithms are applied to solve the problems of controlling the agents' behaviour. Two problems are selected to demonstrate the effectiveness of the proposed algorithms, including the benchmark one, i.e., the Tileworld system, and a real mobile robot control.",
• notes = "also known as \cite{6408015}",

}

Genetic Programming entries for Xianneng Li Shingo Mabu Kotaro Hirasawa

Citations