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113
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 Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues
 IEEE Transactions on Evolutionary Computation
, 2005
"... We recommend you cite the published version. ..."
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Classification of adaptive memetic algorithms: a comparative study
 IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
, 2006
"... Abstract—Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing selfconfiguring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of ..."
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Cited by 68 (8 self)
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Abstract—Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing selfconfiguring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different typelevel meme adaptations using continuous benchmark problems indicate that globallevel adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area. Index Terms—Adaptation, evolutionary algorithm, memetic algorithm, optimization. I.
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 MAXMIN Ant System for the University Course Timetabling Problem
 in Proceedings of the 3rd International Workshop on Ant Algorithm, ANTS 2002, Lecture Notes in Computer Science
, 2002
"... We consider a simplification of a typical university course timetabling problem involving three types of hard and three types of soft constraints. A MAXMIN Ant System, which makes use of a separate local search routine, is proposed for tackling this problem. We devise an appropriate construction gr ..."
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Cited by 50 (0 self)
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We consider a simplification of a typical university course timetabling problem involving three types of hard and three types of soft constraints. A MAXMIN Ant System, which makes use of a separate local search routine, is proposed for tackling this problem. We devise an appropriate construction graph and pheromone matrix representation after considering alternatives. The resulting algorithm is tested over a set of eleven instances from three classes of the problem. The results demonstrate that the ant system is able to construct significantly better timetables than an algorithm that iterates the local search procedure from random starting solutions.
an investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
 Proceedings of the Congress on Evolutionary Computation 2002, CEC 2002
, 2002
"... AbstractThis paper investigates a genetic algorithm based hyperheuristic (hyperGA) for scheduling geographically distributed training staff and courses. The aim of the hyperGA is to evolve a goodquality heuristic for each given instance of the problem and use this to find a solution by applying ..."
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Cited by 42 (12 self)
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AbstractThis paper investigates a genetic algorithm based hyperheuristic (hyperGA) for scheduling geographically distributed training staff and courses. The aim of the hyperGA is to evolve a goodquality heuristic for each given instance of the problem and use this to find a solution by applying a suitable ordering from a set of lowlevel heuristics. Since the user only supplies a number of lowlevel problemspecific heuristics and an evaluation function, the hyperheuristic can easily be reimplemented for a different type of problem, and we would expect it to be robust across a wide range of problem instances. We show that the problem can be solved successfully by a hyperGA, presenting results for four versions of the hyperGA as well as a range of simpler heuristics and applying them to five test data set 1.
Evolving Bin Packing Heuristics with Genetic Programming
 PARALLEL PROBLEM SOLVING FROM NATURE  PPSN IX SPRINGER LECTURE NOTES IN COMPUTER SCIENCE. VOLUME 4193 OF LNCS., REYKJAVIK, ICELAND, SPRINGERVERLAG (2006) 860–869
, 2006
"... The binpacking problem is a well known NPHard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presente ..."
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Cited by 39 (13 self)
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The binpacking problem is a well known NPHard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presented with the sum of the pieces already in the bin and the size of the piece that is about to be packed. This heuristic operates in a fixed framework that iterates through the open bins, applying the heuristic to each one, before deciding which bin to use. The best evolved programs emulate the functionality of the human designed `firstfit' heuristic. Thus, the contribution of this paper is to demonstrate that genetic programming can be employed to automatically evolve bin packing heuristics which are the same as high quality heuristics which have been designed by humans.
A Comprehensive Analysis of Hyperheuristics
"... Abstract. Metaheuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a metaheuristic for solving a problem i ..."
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Cited by 39 (16 self)
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Abstract. Metaheuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a metaheuristic for solving a problem in a certain domain. Hyperheuristics introduce a novel approach for search and optimization. A hyperheuristic method operates on top of a set of heuristics. The most appropriate heuristic is determined and applied automatically by the technique at each step to solve a given problem. Hyperheuristics are therefore assumed to be problem independent and can be easily utilized by nonexperts as well. In this study, a comprehensive analysis is carried out on hyperheuristics. The best method is tested against genetic and memetic algorithms on fourteen benchmark functions. Additionally, new hyperheuristic frameworks are evaluated for questioning the notion of problem independence. 1.
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.