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Content Provider | IEEE Xplore Digital Library |
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Author | Uludag, G. Kiraz, B. Etaner Uyar, A.S. Ozcan, E. |
Copyright Year | 2012 |
Description | Author affiliation: Department of Computer Engineering, Istanbul Technical University, Turkey 34469 (Etaner Uyar, A.S.) || Institute of Science and Technology, Istanbul Technical University, Turkey 34469 (Uludag, G.; Kiraz, B.) || School of Computer Science, University of Nottingham, UK NG8 1BB (Ozcan, E.) |
Abstract | An iterative selection hyper-heuristic method controls and mixes a set of low-level heuristics while solving a given problem. A low-level heuristic is selected and employed for improving a (set of) solution(s) at each step. This study investigates the influence of different heuristic selection methods within a population based incremental learning algorithm and hyper-heuristic based hybrid multiphase framework for solving dynamic environment problems. Even though the hybrid method delivers a good overall performance, it is superior in cyclic environments. The empirical results show that a heuristic selection method that relies on a fixed permutation of the underlying low-level heuristics, combined with a strategy that guarantees diversity when the environment changes is more successful than the learning approaches across different dynamic environments produced using a well known benchmark generator. |
Starting Page | 1 |
Ending Page | 8 |
File Size | 227157 |
Page Count | 8 |
File Format | |
ISBN | 9781467343916 |
e-ISBN | 9781467343923 |
DOI | 10.1109/UKCI.2012.6335755 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-09-05 |
Publisher Place | United Kingdom |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Heuristic algorithms Sociology Educational institutions Vectors Generators Statistics Optimization |
Content Type | Text |
Resource Type | Article |
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