A greedy hyper-heuristic in dynamic environments

Özcan, Ender; Uyar, Şima; Burke, Edmund K.

doi:10.1145/1570256.1570302

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…A preliminary study on the applicability of hyper-heuristics in a dynamic environment was conducted byÖzcan et al [19]. A Greedy hyper-heuristic was used in the experiments.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Selection Hyper-heuristics in Dynamic Environments

Kiraz

Uyar

Özcan

2011

Applications of Evolutionary Computation

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Abstract. Hyper-heuristics are high level methodologies that perform search over the space of heuristics rather than solutions for solving computationally difficult problems. A selection hyper-heuristic framework provides means to exploit the strength of multiple low level heuristics where each heuristic can be useful at different stages of the search. In this study, the behavior of a range of selection hyper-heuristics is investigated in dynamic environments. The results show that hyper-heuristics embedding learning heuristic selection methods are sufficiently adaptive and can respond to different types of changes in a dynamic environment.

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“…A preliminary study on the applicability of hyper-heuristics in a dynamic environment was conducted byÖzcan et al [19]. A Greedy hyper-heuristic was used in the experiments.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Selection Hyper-heuristics in Dynamic Environments

Kiraz

Uyar

Özcan

2011

Applications of Evolutionary Computation

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“…The moving peaks benchmark was used [25]. • Ozcan et al used a greedy selection mechanism, also on simple Gaussian mutational heuristics [11]. The study also considered only spatial and temporal severity of changes.…”

Section: B Hyper-heuristicsmentioning

confidence: 99%

“…The intuitive expectation is that hyper-heuristics should be well-suited to solve dynamic optimisation problems because different heuristic search methods can be brought to bear at different times during the search. Recently a number of studies have investigated this expectation [9] [10] [11] [12] [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Analysis of global information sharing in hyper-heuristics for different dynamic environments

Stockt

Engelbrecht

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Optimisation methods designed for static environments do not perform as well on dynamic optimisation problems as purpose-built methods do. Hyper-heuristics show great promise in handling dynamic environment dynamics because hyper-heuristics adapt to their environment. Different classifications of dynamic environments describe change dynamics such as spatial change severity, temporal change severity, homogeneity of peak movement, etc. Previous studies show that different hyper-heuristic selection mechanisms perform differently across different types of dynamic environments. This study investigates three hyper-heuristic selection methods with different selection pressures and shows an inverse correlation with environment change severity.

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“…There is strong empirical evidence that selection hyper-heuristics work for not only discrete combinatorial problems [3] but also discrete and continuous dynamic environment problems [9,10,15,14], being able to respond to the changes in such an environment rapidly. In this study, we describe a new learning hyperheuristic for dynamic environments, which is designed based on the ant colony optimisation algorithm components.…”

Section: Introductionmentioning

confidence: 99%

An Ant-Based Selection Hyper-heuristic for Dynamic Environments

Kiraz

Etaner-Uyar

Özcan

2013

Applications of Evolutionary Computation

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Abstract. Dynamic environment problems require adaptive solution methodologies which can deal with the changes in the environment during the solution process for a given problem. A selection hyper-heuristic manages a set of low level heuristics (operators) and decides which one to apply at each iterative step. Recent studies show that selection hyperheuristic methodologies are indeed suitable for solving dynamic environment problems with their ability of tracking the change dynamics in a given environment. The choice function based selection hyper-heuristic is reported to be the best hyper-heuristic on a set of benchmark problems. In this study, we investigate the performance of a new learning hyper-heuristic and its variants which are inspired from the ant colony optimisation algorithm components. The proposed hyper-heuristic maintains a matrix of pheromone intensities (utility values) between all pairs of low level heuristics. A heuristic is selected based on the utility values between the previously invoked heuristic and each heuristic from the set of low level heuristics. The ant-based hyper-heuristic performs better than the choice function and even its improved version across a variety of dynamic environments produced by the Moving Peaks Benchmark generator.

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A greedy hyper-heuristic in dynamic environments

Cited by 11 publications

References 11 publications

An Investigation of Selection Hyper-heuristics in Dynamic Environments

An Investigation of Selection Hyper-heuristics in Dynamic Environments

Analysis of global information sharing in hyper-heuristics for different dynamic environments

An Ant-Based Selection Hyper-heuristic for Dynamic Environments

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