A Hyper-heuristic with a Round Robin Neighbourhood Selection

Evolutionary Computation

Burke

2016

Self Cite

Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain.

Section: Controlling Crossover At the Hyper-heuristic Levelmentioning

confidence: 89%

A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem

Drake

Evolutionary Computation

Burke

2016

Self Cite

“…Moreover, there are experimental and theoretical studies showing that mixing move acceptance can yield improved performance [28,36,47]. Hence, in this study, we fix the heuristic selection method as SR and propose an online learning method to partition the low level heuristics, predicting which ones would perform well with a naive move acceptance method and assigning the others to IE.…”

Section: Heuristic Selection and Move Acceptance Methodologiesmentioning

confidence: 99%

“…Kheiri andÖzcan [28] described a bi-stage hyper-heuristic which allows improving and equal moves only in the first stage while a naive move acceptance method allows worsening moves in the following stage.Özcan et al [47] recently combined different move acceptance methods and tested different group decision making strategies as a part of selection hyper-heuristics. One of the rare theoretical studies reveal that mixing move acceptance within a selection hyper-heuristic framework could yield a better running time on some benchmark functions [36].…”

Section: Introductionmentioning

confidence: 99%

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Asta

2015

Information Sciences

Self Cite

Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two equal subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach.

“…Robinhood (round-robin neighbourhood) hyper-heuristic [11] is an iterated multi-stage hyper-heuristic. Robinhood contains three selection hyper-heuristics.…”

Section: Selection Hyper-heuristics For the Extended Hyflex Problem Dmentioning

confidence: 99%

“…This selection hyper-heuristic outperformed eight 'standard' hyper-heuristics across a set of instances from HyFlex problem domains. A detailed description of the Robinhood hyper-heuristic can be found in [11].…”

Section: Selection Hyper-heuristics For the Extended Hyflex Problem Dmentioning

confidence: 99%

Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Almutairi

Communications in Computer and Information Science

Kheiri

et al. 2016

Self Cite

Abstract. Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and performance comparison of heuristic search methods, particularly hyper-heuristics. In this study, the performance of six previously proposed selection hyper-heuristics are evaluated on three recently introduced extended HyFlex problem domains, namely 0-1 Knapsack, Quadratic Assignment and Max-Cut. The empirical results indicate the strong generalising capability of two adaptive selection hyper-heuristics which perform well across the 'unseen' problems in addition to the six standard HyFlex problem domains.