A Hyperheuristic Approach to Scheduling a Sales Summit

Cowling, Peter I.; Kendall, Graham; Soubeiga, Eric

doi:10.1007/3-540-44629-x_11

Cited by 409 publications

(468 citation statements)

References 12 publications

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“…Thus, meta-heuristics may have different performances on different problem domains or even on different instances of the same problem. In order to overcome these dependencies, automated search techniques have emerged [8][9][10], and now are often generically called hyper-heuristics. Hyper-heuristics take the search process one level higher to the space of heuristics.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Generalizing Hyper-heuristics via Apprenticeship Learning

Asta

Özcan

Parkes

et al. 2013

Evolutionary Computation in Combinatorial Optimization

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Abstract. An apprenticeship-learning-based technique is used as a hyperheuristic to generate heuristics for an online combinatorial problem. It observes and learns from the actions of a known-expert heuristic on small instances, but has the advantage of producing a general heuristic that works well on other larger instances. Specifically, we generate heuristic policies for online bin packing problem by using expert near-optimal policies produced by a hyper-heuristic on small instances, where learning is fast. The "expert" is a policy matrix that defines an index policy, and the apprenticeship learning is based on observation of the action of the expert policy together with a range of features of the bin being considered, and then applying a k-means classification. We show that the generated policy often performs better than the standard best-fit heuristic even when applied to instances much larger than the training set.

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Section: Introduction and Related Workmentioning

confidence: 99%

Generalizing Hyper-heuristics via Apprenticeship Learning

Asta

Özcan

Parkes

et al. 2013

Evolutionary Computation in Combinatorial Optimization

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“…The Robinhood hyper-heuristic is composed of components inspired from previously proposed approaches. The heuristic selection methods presented by Cowling et al [6] includes Random Permutation and Random Permutation Gradient. This method applies a low level heuristic one at a time sequentially in a randomly generated permutation order.…”

Section: Methodsmentioning

confidence: 99%

“…A selection hyper-heuristic methodology combines heuristic selection and move acceptance processes under a single point search framework [5][6][7][8][9]. A candidate solution is improved iteratively by selecting, applying a heuristic (neighbourhood operator) from a set of low level heuristics, then passing the new solution through move acceptance to replace the solution in hand at each step.…”

Section: Introductionmentioning

confidence: 99%

A Hyper-heuristic with a Round Robin Neighbourhood Selection

Kheiri

Özcan

2013

Evolutionary Computation in Combinatorial Optimization

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Abstract. An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.

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“…We call the operator every m/20th iteration of the algorithm, where m equals the maximum number of iterations with no improvement to the cost function. The idea of shuffling is the same as in hyperheuristics [22] but the other way around. Hyperheuristic is a mechanism that chooses a heuristic from a set of simple heuristics, applies it to the current solution to get a better solution, then chooses another heuristic and applies it, and continues this iterative cycle until the termination criterion is satisfied.…”

Section: The Peast Algorithm and Its Componentsmentioning

confidence: 99%

Crucial Components of the PEAST Algorithm in Solving Real-World Scheduling Problems

Kyngäs¹,

Nurmi²,

Kyngäs³

2013

LNSE

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Abstract-A large number of metaheuristics and local search methods have been developed for combinatorial and global optimization. We present our PEAST algorithm which is capable of solving very difficult real-world scheduling problems, such as workforce scheduling, sports scheduling and school timetabling. The goal of this paper is to identify the crucial components of the PEAST algorithm. We believe that recognizing the importance of these components helps other researchers strengthen their population-based and local search methods. IndexTerms-PEAST algorithm, local search, population-based methods, real-world scheduling.

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A Hyperheuristic Approach to Scheduling a Sales Summit

Cited by 409 publications

References 12 publications

Generalizing Hyper-heuristics via Apprenticeship Learning

Generalizing Hyper-heuristics via Apprenticeship Learning

A Hyper-heuristic with a Round Robin Neighbourhood Selection

Crucial Components of the PEAST Algorithm in Solving Real-World Scheduling Problems

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