研究者詳細 - 上野　史

論文 - 上野　史

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A modified cuckoo search algorithm for dynamic optimization problems. 査読

Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Keiki Takadama

IEEE Congress on Evolutionary Computation(CEC) 1757 - 1764 2016年

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

This paper proposes a simple modification of the Cuckoo Search called CS for a dynamic environment. In this paper, we consider a dynamic optimization problem where the global optimum can be cyclically changed depending on time. Our modified CS algorithm holds good candidates in order to effectively explore the search space near those candidates with an intensive local search. Our first experiment tests the prosed method on a set of static optimization problems, which aims at evaluating the potential performance of the proposed method. Then, we apply it to a dynamic optimization problem. Experimental results on the static problems show that the proposed method derives a better performance than the conventional method, which suggest the proposed method potentially has a good capability of finding a good solution. On the dynamic problem, the proposed method also performs well while the conventional method fails to find a better solution.

DOI： 10.1109/CEC.2016.7744001

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Proceedings in Adaptation, Learning and Optimization.

Fumito Uwano, Keiki Takadama

Intelligent and Evolutionary Systems - The 20th Asia Pacific Symposium(IES) 453 - 467 2016年

Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach. 査読

Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs

BICT 2015, Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)(BICT) 332 - 339 2015年

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