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43
A TabuSearch Hyperheuristic for Timetabling and Rostering
, 2003
"... Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. The main motivation behind the development of such approaches is the goal of developing automated scheduling methods which are not restricted to one problem. In this paper ..."
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Cited by 148 (60 self)
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Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. The main motivation behind the development of such approaches is the goal of developing automated scheduling methods which are not restricted to one problem. In this paper we report the investigation of a hyperheuristic approach and evaluate it on various instances of two distinct timetabling and rostering problems. In the framework of our hyperheuristic approach, heuristics compete using rules based on the principles of reinforcement learning. A tabu list of heuristics is also maintained which prevents certain heuristics from being chosen at certain times during the search. We demonstrate that this tabusearch hyperheuristic is an easily reusable method which can produce solutions of at least acceptable quality across a variety of problems and instances. In effect the proposed method is capable of producing solutions that are competitive with those obtained using stateof theart problemspecific techniques for the problems studied here, but is fundamentally more general than those techniques.
HyperHeuristics: An Emerging Direction In Modern Search Technology
, 2003
"... This chapter introduces and overviews an emerging methodology in search and optimisation. One of the key aims of these new approaches, which have been termed hyperheuristics, is to raise the level of generality at which optimisation systems can operate. An objective is that hyperheuristics will le ..."
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Cited by 119 (41 self)
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This chapter introduces and overviews an emerging methodology in search and optimisation. One of the key aims of these new approaches, which have been termed hyperheuristics, is to raise the level of generality at which optimisation systems can operate. An objective is that hyperheuristics will lead to more general systems that are able to handle a wide range of problem domains rather than current metaheuristic technology which tends to be customised to a particular problem or a narrow class of problems. Hyperheuristics are broadly concerned with intelligently choosing the right heuristic or algorithm in a given situation. Of course, a hyperheuristic can be (often is) a (meta)heuristic and it can operate on (meta)heuristics. In a certain sense, a hyperheuristic works at a higher level when compared with the typical application of metaheuristics to optimisation problems i.e. a hyperheuristic could be thought of as a (meta)heuristic which operates on lower level (meta )heuristics. In this chapter we will introduce the idea and give a brief history of this emerging area. In addition, we will review some of the latest work to be published in the field.
A Classification of Hyperheuristic Approaches
"... The current state of the art in hyperheuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In ..."
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Cited by 55 (22 self)
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The current state of the art in hyperheuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyperheuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyperheuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyperheuristic research.
A Monte Carlo HyperHeuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine
 PLACEMENT MACHINE, INTECH’03 THAILAND
, 2003
"... In this paper we introduce a Monte Carlo based hyperheuristic. The Monte Carlo hyperheuristic manages a set of low level heuristics (in this case just simple 2opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that ..."
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Cited by 39 (12 self)
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In this paper we introduce a Monte Carlo based hyperheuristic. The Monte Carlo hyperheuristic manages a set of low level heuristics (in this case just simple 2opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that may be impossible to create by the other low level heuristics. On each iteration, the Monte Carlo hyper heuristic randomly calls a low level heuristic. The new solution returned by the low level heuristic will be accepted based on the Monte Carlo acceptance criteria. The Monte Carlo acceptance criteria always accept an improved solution. Worse solutions will be accepted with a certain probability, which decreases with worse solutions, in order to escape local minima. We develop three hyperheuristics based on a Monte Carlo method, these being Linear Monte Carlo Exponential Monte Carlo and Exponential Monte Carlo with counter. We also investigate four other hyperheuristics to examine their performance and for comparative purposes. To demonstrate our approach we employ these hyperheuristics to optimise component placement sequencing in order to improve the efficiency of the multi head placement machine. Experimental results show that the Exponential Monte Carlo hyperheuristic is superior to the other hyperheuristics and is superior to a choice function hyperheuristic reported in earlier work.
Hybrid variable neighbourhood approaches to university exam timetabling
, 2006
"... Abstract. In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with ..."
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Cited by 25 (9 self)
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Abstract. In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with a Genetic Algorithm. A number of different neighbourhood structures are analysed. It is demonstrated that the proposed technique is able to produce high quality solutions across a wide range of benchmark problem instances. In particular, we demonstrate that the Genetic Algorithm, which intelligently selects approporiate neighbourhoods to use within the biased VNS produces the best known results in the literature, in terms of solution quality, on some of the the benchmark instances, although it requires relatively large amount of computational time. Possible extensions to this overall approach are also discussed. 1
Distributed Choice Function Hyperheuristics for Timetabling and Scheduling
 Practice and Theory of Automated Timetabling V, Springer Lecture notes in Computer Science. Volume 3616. (2005) 51–67
, 2004
"... This paper reports on ongoing research in the design of choice function hyperheuristics, the modelling of generalpurpose low level heuristics, and the exploitation of parallel computing platforms for hyperheuristics ..."
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Cited by 23 (1 self)
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This paper reports on ongoing research in the design of choice function hyperheuristics, the modelling of generalpurpose low level heuristics, and the exploitation of parallel computing platforms for hyperheuristics
An investigation of automated planograms using a simulated annealing based hyperheuristics
 Progress as Real Problem Solver  (Operations Research/Computer Science Interface Serices, Vol.32
, 2005
"... This paper formulates the shelf space allocation problem as a nonlinear function of the product net profit and storeinventory. We show that this model is an extension of multiknapsack problem, which is itself an NPhard problem. A twostage relaxation is carried out to get an upper bound of the m ..."
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Cited by 22 (10 self)
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This paper formulates the shelf space allocation problem as a nonlinear function of the product net profit and storeinventory. We show that this model is an extension of multiknapsack problem, which is itself an NPhard problem. A twostage relaxation is carried out to get an upper bound of the model. A simulated annealing based hyperheuristic algorithm is proposed to solve several problem instances with different problem sizes and space ratios. The results show that the simulated annealing hyperheuristic significantly outperforms two conventional simulated annealing algorithms and other hyperheuristics for all problem instances. The experimental results show that our approach is a robust and efficient approach for the shelf space allocation problem. hyperheuristics, simulated annealing, shelf space allocation, planograms 1.
Investigation of a Tabu Assisted HyperHeuristic Genetic Algorithm
 In proceedings of Congress on Evolutionary Computation(CEC2003
, 2003
"... AbstractThis paper investigates a tabu assisted genetic algorithm based hyperheuristic (hyperTGA) for personnel scheduling problems. We recently introduced a hyperheuristic genetic algorithm (hyperGA) with an adaptive length chromosome which aims to evolve an ordering of lowlevel heuristics in ..."
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Cited by 21 (5 self)
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AbstractThis paper investigates a tabu assisted genetic algorithm based hyperheuristic (hyperTGA) for personnel scheduling problems. We recently introduced a hyperheuristic genetic algorithm (hyperGA) with an adaptive length chromosome which aims to evolve an ordering of lowlevel heuristics in order to find good quality solutions to given problems. The addition of a tabu method, the focus of this paper, extends that work. The aim of adding a tabu list to the hyperGA is to indicate the efficiency of each gene within the chromosome. We apply the algorithm to a geographically distributed training staff and course scheduling problem and compare the computational results with our previous hyperGA. 1.
An Ant Algorithm Hyperheuristic for the Project Presentation Scheduling Problem
 In: Proceedings of the Congress on Evolutionary Computation 2005 (CEC’05). Volume 3
, 2005
"... Ant algorithms have generated significant research interest within the search/optimisation community in recent years. Hyperheuristic research is concerned with the development of "heuristics to choose heuristics" in an attempt to raise the level of generality at which optimisation systems ..."
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Cited by 17 (5 self)
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Ant algorithms have generated significant research interest within the search/optimisation community in recent years. Hyperheuristic research is concerned with the development of "heuristics to choose heuristics" in an attempt to raise the level of generality at which optimisation systems can operate. In this paper the two are brought together. An investigation of the ant algorithm as a hyperheuristic is presented and discussed. The results are evaluated against other hyperheuristic methods, when applied to a real world scheduling problem.
An Adaptive Length Chromosome Hyperheuristic Genetic Algorithm for a Trainer Scheduling Problem
 Proceedings of the fourth AsiaPacific Conference on Simulated Evolution And Learning, (SEAL'02), Orchid Country Club, Singapore, 1822 Nov 2002
"... HyperGA was introduced by the authors as a genetic algorithm based hyperheuristic which aims to evolve an ordering of lowlevel heuristics so as to find a good quality solution to a given problem. The adaptive length chromosome hyperGA, let's call it ALChyperGA, is an extension of the author ..."
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Cited by 17 (5 self)
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HyperGA was introduced by the authors as a genetic algorithm based hyperheuristic which aims to evolve an ordering of lowlevel heuristics so as to find a good quality solution to a given problem. The adaptive length chromosome hyperGA, let's call it ALChyperGA, is an extension of the authors previous work, in which the chromosome was of fixed length. The aim of a variable length chromosome is two fold; 1) it allows dynamic removal and insertion of heuristics 2) it allows the GA to find a good chromosome length which could otherwise only be found by experimentation. We apply the ALChyperGA to a geographically distributed training staff and courses scheduling problem, and report that good quality solution can be found. We also present results for four versions of the ALChyperGA, applied to five test data sets.