ABSTRACT
If an optimisation algorithm performs a search in an environment that changes over time, it should be able to follow these changes and adapt itself for handling them in order to achieve good results. Different types of dynamics in a changing environment require the use of different approaches. Hyper-heuristics represent a class of methodologies that are high level heuristics performing search over a set of low level heuristics. Due to the generality of hyper-heuristic frameworks, they are expected to be adaptive. Hence, a hyper-heuristic can be used in a dynamic environment to determine the approach to apply, adapting itself accordingly at each change. This study presents an initial investigation of hyper-heuristics in dynamic environments. A greedy hyper-heuristic is tested over a set of benchmark functions.
- ]]J. Branke. Evolutionary optimisation in Dynamic Environments. Kluwer, 2001. Google ScholarDigital Library
- ]]E. K. Burke, E. Hart, G. Kendall, J. Newall, P. Ross, and S. Schulenburg. Hyper-heuristics: An emerging direction in modern search technology. In Handbook of Metaheuristics, pages 457--474. Kluwer, 2003.Google Scholar
- ]]E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and J. R. Woodward. Exploring Hyper-heuristic methodologies with genetic programming. In Studies in Computational Intelligence: collaboration, fusion and emergence, chapter 6. Springer, 2009.Google Scholar
- ]]P. Cowling, G. Kendall, and E. Soubeiga. A Hyper-heuristic approach for scheduling a sales summit. In Selected Papers of the Third International Conference on the Practice And Theory of Automated Timetabling, PATAT 2000, Lecture Notes in Computer Science, pages 176--190, Springer, 2000. Google ScholarDigital Library
- ]]P. Cowling, G. Kendall, and E. Soubeiga. Hyper-heuristics: A tool for rapid prototyping in scheduling and optimisation. In Applications of Evolutionary Computing: Proceeding of Evo Workshops 2002, volume 2279 of Lecture Notes in Computer Science, pages 1--10, Springer, 2002. Google ScholarDigital Library
- ]]L. Davis. Bit climbing, representational bias, and test suite design. In Proceedings of the 4th Int. Conference on Genetic Algorithms, pages 18--23, 1991.Google Scholar
- ]]Y. Jin and J. Branke. Evolutionary optimisation in uncertain environments -- a survey. IEEE Transactions on Evolutionary Computation, 9(3):303--317, 2005. Google ScholarDigital Library
- ]]R. Morrison. Designing Evolutionary Algorithms for Dynamic Environments. Springer, 2004. Google ScholarDigital Library
- ]]E. Ozcan, B. Bilgin, and E. E. Korkmaz. Hill climbers and mutational heuristics in Hyper-heuristics. In Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN 2006), volume 4193 of Lecture Notes in Computer Science, pages 202--211, Springer, 2006. Google ScholarDigital Library
- ]]E. Ozcan, B. Bilgin, and E. E. Korkmaz. A comprehensive survey of Hyper-heuristics. Intelligent Data Analysis, 12(1):1--21, 2008. Google ScholarDigital Library
- ]]K. V. Price and R. M. Storn and J. A. Lampinen. Differential Evolution, A Practical Approach to Global optimisation. Springer, 2005. Google ScholarDigital Library
- ]]K. Weicker, editor. Evolutionary Algorithms and Dynamic optimisation Problems. Der Andere Verlag, 2003.Google Scholar
- ]]S. Yang, Y.-S. Ong, and Y. Jin, editors. Evolutionary Computation in Dynamic and Uncertain Environments, volume 51 of Studies in Computational Intelligence. Springer, 2004. Google ScholarDigital Library
Index Terms
- A greedy hyper-heuristic in dynamic environments
Recommendations
A Hyper-Heuristic Using GRASP with Path-Relinking: A Case Study of the Nurse Rostering Problem
The goal of hyper-heuristics is to design and choose heuristics to solve complex problems. The primary motivation behind the hyper-heuristics is to generalize the solving ability of the heuristics. In this paper, the authors propose a Hyper-heuristic ...
A framework to hybridize PBIL and a hyper-heuristic for dynamic environments
PPSN'12: Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part IISelection hyper-heuristic methodologies explore the space of heuristics which in turn explore the space of candidate solutions for solving hard computational problems. This study investigates the performance of approaches based on a framework that ...
A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem
Hyper-heuristics introduce novel approaches for solving challenging combinatorial optimization problems by operating over a set of low level (meta)-heuristics. This is achieved by an evolutionary selection mechanism that controls and combines the ...
Comments