Big-valley Explorer: A Framework of Real-coded Genetic Algorithms for Multi-funnel Function Optimization
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- Uemura Kento
- Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology; Research Fellowship for Young Scientists (DC1) of the Japan Society for the Promotion of Science
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- Kinoshita Shun-ichi
- Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology (Present: Cyberscience Center, Tohoku University)
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- Nagata Yuichi
- Education Academy of Computational Life Sciences, Tokyo Institute of Technology
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- Kobayashi Shigenobu
- Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology (Present: Professor Emeritus, Tokyo Institute of Technology)
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- Ono Isao
- Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
Bibliographic Information
- Other Title
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- 大域的多峰性関数最適化のための実数値GAの枠組みBig-valley Explorerの提案
Description
This paper proposes a new framework of real-coded genetic algorithms (RCGAs) for the multi-funnel function optimization. The RCGA is one of the most powerful function optimization methods. Most conventional RCGAs work effectively on the single-funnel function that consists of a single big-valley. However, it is reported that they show poor performance or, sometimes, fail to find the optimum on the multi-funnel function that consists of multiple big-valleys. In order to remedy this deterioration, Innately Split Model (ISM) has been proposed as a framework of RCGAs. ISM initializes an RCGA in a small region and repeats a search with the RCGA as changing the position of the region randomly. ISM outperforms conventional RCGAs on the multi-funnel functions. However, ISM has two problems in terms of the search efficiency and the difficulty of setting parameters. Our proposed method, Big-valley Explorer (BE), is a framework of RCGAs like ISM and it has two novel mechanisms to overcome these problems, the big-valley estimation mechanism and the adaptive initialization mechanism. Once the RCGA finishes a search, the big-valley estimation mechanism estimates a big-valley that the RCGA already explored and removes the region from the search space to prevent the RCGA from searching the same big-valley many times. After that, the adaptive initialization mechanism initializes the RCGA in a wide unexplored region adaptively to find unexplored big-valleys. We evaluate BE through some numerical experiments with both single-funnel and multi-funnel benchmark functions.
Journal
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- Transaction of the Japanese Society for Evolutionary Computation
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Transaction of the Japanese Society for Evolutionary Computation 4 (1), 1-12, 2013
The Japanese Society for Evolutionary Computation
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Details 詳細情報について
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- CRID
- 1390001205366429312
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- NII Article ID
- 130004965145
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- ISSN
- 21857385
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed