Hill Climbers and Mutational Heuristics in Hyperheuristics

Özcan, Ender; Bi̇lgi̇n, Burak; Korkmaz, Emin Erkan

doi:10.1007/11844297_21

Cited by 42 publications

(24 citation statements)

References 8 publications

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“…Although there are more elaborate move acceptance methods, for example, Monte-Carlo based move acceptance strategy [7], Simulated Annealing [10], Late Acceptance [11], there is strong empirical evidence that combining simple components under a selection hyper-heuristic framework with the right low level heuristics could still yield an improved performance. [46] shows that the performance of a selection hyper-heuristic could vary if the set of low level heuristics change, as expected.…”

Section: Heuristic Selection and Move Acceptance Methodologiessupporting

confidence: 72%

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

Asta

Özcan

2015

Information Sciences

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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.

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Section: Heuristic Selection and Move Acceptance Methodologiessupporting

confidence: 72%

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

Asta

Özcan

2015

Information Sciences

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“…In the literature, these include both moving strategies (e.g. [3,9,16,26,30]) and constructive strategies (e.g. [6,12,16,27]).…”

Section: Introductionmentioning

confidence: 99%

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

Burke

McCollum

2009

European Journal of Operational Research

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In this paper we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences that consist of different graph colouring heuristics to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different heuristics to construct solutions step by step. Based on these sequences, we analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research.It is observed that spending the effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate good quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme which is concerned with developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 Abstract. In this paper we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences that consist of different graph colouring heuristics to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different heuristics to construct solutions step by step. Based on these sequences, we analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate good quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme which is concerned with developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. ...

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“…In our case, 1-city insertion, 2-exchange, arbitrary insertion and inver-over are the stochastic low-level heuristics, whilst 2-opt, 3-opt, OR-opt and node insertion are the deterministic ones. These eight low-level heuristics were implemented as defined in [1,3,10,15,19] and operating in a hill climber style [12].…”

Section: Methods and Resultsmentioning

confidence: 99%

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

Terrazas

Landa-Silva

Krasnogor

2010

Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)

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The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for specific problems, that is, the input to the algorithm are problems and the output are problem-tailored heuristics. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Thus, hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem in hand. Some approaches like genetic programming have been proposed for this. In this paper, we report on an alternative methodology that sheds light on simple methodologies that efficiently cooperate by means of local interactions. These entities are seen as building blocks, the combination of which is employed for the automated manufacture of good performing heuristic search strategies. We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem.

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Hill Climbers and Mutational Heuristics in Hyperheuristics

Cited by 42 publications

References 8 publications

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

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

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

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