2017年12月
Adaptive Distributed Modified Extremal Optimization for Maximizing Contact Map Overlap and Its Performance Evaluation
“Innovative Computational Intelligence Methods for Data Sciences and Applications" in International Journal of Computational Intelligence Studies (IJCIStudies)
- ,
- ,
- 巻
- Vol. 6
- 号
- No. 4
- 開始ページ
- 288
- 終了ページ
- 310
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1504/IJCISTUDIES.2017.089518
- 出版者・発行元
- Inderscience Enterprises Ltd.
Maximising the contact map overlap (CMO) problem is one of the simplest yet most robust techniques for finding optimal protein structure alignment. This optimisation is known as the CMO problem, and is also known as NP-hard. We have been developing bio-inspired heuristics using distributed modified extremal optimisation (DMEO) for the CMO problem. DMEO is a hybrid of population-based modified extremal optimisation (PMEO) and the island model. In our previous work, we proposed a DMEO-based bio-inspired heuristic, i.e., DMEO with different evolutionary strategies (DMEODES) to maintain the population diversity of evolution. DMEODES efficiently maintains population diversity; however, once the population falls into local optimal solutions, there is no mechanism for getting out of them. In this paper, we propose a novel heuristic model to improve the DMEO's ability to prevent evolution stagnation. The new model integrates an adaptive generation alternation mechanism in DMEO called ADMEO. The experimental results show that ADMEO outperforms DMEODES.
- ID情報
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- DOI : 10.1504/IJCISTUDIES.2017.089518
- ISSN : 1755-4985