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Heuristic selection in a multi-phase hybrid approach for dynamic environments.
| Content Provider | CiteSeerX |
|---|---|
| Author | Kiraz, Berna Özcan, Ender |
| Abstract | 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 cyclic dynamic environments produced by a well known benchmark generator. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Heuristic Selection Multi-phase Hybrid Approach Dynamic Environment Low-level Heuristic Hybrid Multiphase Framework Empirical Result Iterative Selection Hyper-heuristic Method Control Heuristic Selection Method Cyclic Environment Different Cyclic Dynamic Environment Benchmark Generator Fixed Permutation Good Overall Performance Incremental Learning Algorithm Different Heuristic Selection Method Dynamic Environment Problem Hybrid Method Delivers Environment Change |
| Content Type | Text |
