Selection hyper-heuristics in dynamic environments

B Kiraz, A Ş Etaner-Uyar and E Özcan
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B Kiraz: Institute of Science and Technology, Istanbul Technical University, Istanbul, Turkey
A Ş Etaner-Uyar: Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
E Özcan: University of Nottingham, Nottingham, UK

Journal of the Operational Research Society, 2013, vol. 64, issue 12, 1753-1769

Abstract: Current state-of-the-art methodologies are mostly developed for stationary optimization problems. However, many real-world problems are dynamic in nature, where different types of changes may occur over time. Population-based approaches, such as evolutionary algorithms, are frequently used for solving dynamic environment problems. Selection hyper-heuristics are highly adaptive search methodologies that aim to raise the level of generality by providing solutions to a diverse set of problems having different characteristics. In this study, the performances of 35 single-point-search-based selection hyper-heuristics are investigated on continuous dynamic environments exhibiting various change dynamics, produced by the Moving Peaks Benchmark generator. Even though there are many successful applications of selection hyper-heuristics to discrete optimization problems, to the best of our knowledge, this study is one of the initial applications of selection hyper-heuristics to real-valued optimization as well as being among the very few which address dynamic optimization issues using these techniques. The empirical results indicate that learning selection hyper-heuristics incorporating compatible components can react to different types of changes in the environment and are capable of tracking them. This study shows the suitability of selection hyper-heuristics as solvers in dynamic environments.

Date: 2013
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