A hybrid multi-population framework for dynamic environments combining online and offline learning

作     者：Uludag, Gonul Kiraz, Berna Etaner-Uyar, A. Sima Ozcan, Ender 

作者机构：Istanbul Tech Univ Inst Sci & Technol TR-34469 Istanbul Turkey Istanbul Tech Univ Dept Comp Engn TR-34469 Istanbul Turkey Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2013年第17卷第12期

页      面：2327-2348页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：EPSRC [EP/F033214/1] TUBITAK EPSRC [EP/F033214/1, EP/H000968/1] Funding Source: UKRI 

主　　题：Heuristic Metaheuristic Hyper-heuristic Estimation of distribution algorithm Dynamic environment 

摘      要：Population based incremental learning algorithms and selection hyper-heuristics are highly adaptive methods which can handle different types of dynamism that may occur while a given problem is being solved. In this study, we present an approach based on a multi-population framework hybridizing these methods to solve dynamic environment problems. A key feature of the hybrid approach is the utilization of offline and online learning methods at successive stages. The performance of our approach along with the influence of different heuristic selection methods used within the selection hyper-heuristic is investigated over a range of dynamic environments produced by a well known benchmark generator as well as a real world problem, referred to as the Unit Commitment Problem. The empirical results show that the proposed approach using a particular hyper-heuristic outperforms some of the best known approaches in literature on the dynamic environment problems dealt with.