An empirical study of hyperheuristics for managing very large sets of low level heuristics

Remde, Stephen; Cowling, Peter I.; Dahal, Keshav; Colledge, Nic; Selensky, Evgeny

doi:10.1057/jors.2011.48

Cited by 20 publications

(9 citation statements)

References 25 publications

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“…In the cases examined, this trade-off was beneficial as the hyper-heuristic was able to find more optimal solutions when provided with additional genetic material. If at some point this trade-off no longer is beneficial, then reducing/partitioning the primitives may become useful [17]. It was also found that the arity of the genetic material can have a large impact on the increase in search space.…”

Section: Discussionmentioning

confidence: 99%

Hyper-Heuristics

Rivers

Tauritz

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Practitioners often need to solve real world problems for which no custom search algorithms exist. In these cases they tend to use general-purpose solvers that have no guarantee to perform well on their specific problem. The relatively new field of hyper-heuristics provides an alternative to the potential pit-falls of general-purpose solvers, by allowing practitioners to generate a custom algorithm optimized for their problem of interest. Hyper-heuristics are meta-heuristics operating on algorithm space employing targeted primitives to compose algorithms. This paper explores the advantages and disadvantages of expanding a hyperheuristic's primitive-space with additional primitives. This should allow for an increase in quality of evolved algorithms. However, increasing the search space of a meta-heuristic almost always results in longer time to convergence and lower quality results for the same amount of computational time, but also all too often lower quality results at convergence, potentially making a problem impractical to solve for a practitioner. This paper explores the scalability of hyperheuristics as the primitive-space is increased, demonstrating significantly increased quality solutions at convergence with a corresponding increase in convergence time. Additionally, this paper explores the impact that the nature of the added primitives have on the performance of the hyper-heuristic.

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Section: Discussionmentioning

confidence: 99%

Hyper-Heuristics

Rivers

Tauritz

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…However, there are methodologies that can cut across categories. For example, we can see hybrid methodologies that combine constructive with perturbation heuristics (see eg Garrido and Riff, 2010), or heuristic selection with heuristic generation (Krasnogor and Gustafson, 2004;Maturana et al, 2010;Remde et al, 2012).…”

Section: Figurementioning

confidence: 99%

“…In Remde et al (2009) a tabu search-based hyper-heuristic dynamically adapting the tabu tenures is applied to the same problem and framework. A comprehensive study on this framework is presented in Remde et al (2012), where several hyper-heuristics are compared against a Variable Neighbourhood and a Greedy Selection method with favourable results. The best performing hyper-heuristics depend on the allotted CPU time.…”

Section: Approaches Based On Constructive Low-level Heuristicsmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Burke

Gendreau

Hyde

et al. 2013

Journal of the Operational Research Society

964

550

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Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

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“…Due to the large number of experiments and the long training time required, a modified master-slave parallel GA (used in [40]) was used with a network of 35 PCs to speed up this stage of training. For example, the completion of one generation using only one PC takes approximately 450 s, which is decreased sharply to approximately 19 s with 35 PCs.…”

Section: Learning Gafnnmentioning

confidence: 99%

GA-based learning for rule identification in fuzzy neural networks

Dahal

Almejalli

Hossain

et al. 2015

Applied Soft Computing

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a b s t r a c tEmploying an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.

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An empirical study of hyperheuristics for managing very large sets of low level heuristics

Cited by 20 publications

References 25 publications

Hyper-Heuristics

Hyper-Heuristics

Hyper-heuristics: a survey of the state of the art

GA-based learning for rule identification in fuzzy neural networks

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