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Recent Research Directions in Automated Timetabling
 ACCEPTED FOR PUBLICATION IN EUROPEAN JOURNAL OF OPERATIONAL RESEARCH – EJOR
, 2002
"... The aim of this paper is to give a brief introduction to some recent approaches to timetabling problems that have been developed or are under development in the Automated Scheduling, Optimisation and Planning Research Group (ASAP) at the University of Nottingham. We have concentrated upon university ..."
Abstract

Cited by 97 (41 self)
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The aim of this paper is to give a brief introduction to some recent approaches to timetabling problems that have been developed or are under development in the Automated Scheduling, Optimisation and Planning Research Group (ASAP) at the University of Nottingham. We have concentrated upon university timetabling but we believe that some of the methodologies that are described can be used for different timetabling problems such as employee timetabling, timetabling of sports fixtures, etc. The paper suggests a number of approaches and comprises three parts. Firstly, recent heuristic and evolutionary timetabling algorithms are discussed. In particular, two evolutionary algorithm developments are described: a method for decomposing large realworld timetabling problems and a method for heuristic initialisation of the population. Secondly, an approach that considers timetabling problems as multicriteria decision problems is presented. Thirdly, we discuss a casebased reasoning approach that employs previous experience to solve new timetabling problems. Finally, we outline some new research ideas and directions in the field of timetabling. The overall aim of these research directions is to explore approaches that can operate at a higher level of generality than is currently possible.
A MultiStage Evolutionary Algorithm for the Timetable Problem
, 1998
"... It is well known that timetabling problems can be particularly difficult to solve, especially when dealing with particularly large instances. Finding near optimal results can prove to be extremely difficult, even when using advanced search methods such as evolutionary algorithms (EAs). In this paper ..."
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Cited by 79 (31 self)
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It is well known that timetabling problems can be particularly difficult to solve, especially when dealing with particularly large instances. Finding near optimal results can prove to be extremely difficult, even when using advanced search methods such as evolutionary algorithms (EAs). In this paper we present a method of decomposing larger problems into smaller components, each of which is of a size that the EA can effectively handle. We will show various experimental results using this method to prove that not only can the execution time be considerably reduced but also that the presented method can actually improve the quality of produced solutions.
Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem
, 1999
"... In this paper, a fitness landscape analysis for several instances of the quadratic assignment problem (QAP) is performed and the results are used to classify problem instances according to their hardness for local search heuristics and metaheuristics based on local search. The local properties of t ..."
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Cited by 62 (9 self)
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In this paper, a fitness landscape analysis for several instances of the quadratic assignment problem (QAP) is performed and the results are used to classify problem instances according to their hardness for local search heuristics and metaheuristics based on local search. The local properties of the tness landscape are studied by performing an autocorrelation analysis, while the global structure is investigated by employing a fitness distance correlation analysis. It is shown that epistasis, as expressed by the dominance of the flow and distance matrices of a QAP instance, the landscape ruggedness in terms of the correlation length of a landscape, and the correlation between fitness and distance of local optima in the landscape together are useful for predicting the performance of memetic algorithms  evolutionary algorithms incorporating local search  to a certain extent. Thus, based on these properties a favorable choice of recombination and/or mutation operators can be found.
Fitness Variance of Formae and Performance Prediction
, 1994
"... Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representationindependentoperators allows the formulation of formal representationindependent evolutionary algorithms. These formal algorithms can be instantiated ..."
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Cited by 61 (7 self)
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Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representationindependentoperators allows the formulation of formal representationindependent evolutionary algorithms. These formal algorithms can be instantiated for particular search problems by selecting a suitable representation. The performance of different representations, in the context of any given formal representationindependent algorithm, can then be measured. Simple analyses suggest that fitness variance of formae (generalised schemata) for the chosen representation might act as a performance predictor for evolutionary algorithms. This hypothesis is tested and supported through studies of four different representations for the travelling salesrep problem (TSP) in the context of both formal representationindependentgenetic algorithms and corresponding memetic algorithms. 1 Motivation The subject of this paper is representation i...
Formal Memetic Algorithms
, 1994
"... A formal, representationindependent form of a memetic algorithm a genetic algorithm incorporating local searchis introduced. A generalised form of Npoint crossover is defined together with representationindependent patching and hillclimbing operators. The resulting formal algorithm is t ..."
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Cited by 50 (4 self)
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A formal, representationindependent form of a memetic algorithm a genetic algorithm incorporating local searchis introduced. A generalised form of Npoint crossover is defined together with representationindependent patching and hillclimbing operators. The resulting formal algorithm is then constructed and tested empirically on the travelling salesrep problem. Whereas the genetic algorithms tested were unable to make good progress on the problems studied, the memetic algorithms performed very well.
Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies
, 2001
"... ..."
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
 Evolutionary Computation
, 2000
"... The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis ..."
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Cited by 46 (13 self)
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The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced.
A Comparison of Memetic Algorithms, Tabu Search, and Ant Colonies for the Quadratic Assignment Problem
 Proc. Congress on Evolutionary Computation, IEEE
, 1999
"... A memetic algorithm (MA), i.e. an evolutionary algorithm making use of local search, for the quadratic assignment problem is presented. A new recombination operator for realizing the approach is described, and the behavior of the MA is investigated on a set of problem instances containing between 25 ..."
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Cited by 33 (4 self)
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A memetic algorithm (MA), i.e. an evolutionary algorithm making use of local search, for the quadratic assignment problem is presented. A new recombination operator for realizing the approach is described, and the behavior of the MA is investigated on a set of problem instances containing between 25 and 100 facilities/locations. The results indicate that the proposed MA is able to produce high quality solutions quickly. A comparison of the MA with some of the currently best alternative approaches  reactive tabu search, robust tabu search and the fast ant colony system  demonstrates that the MA outperforms its competitors on all studied problem instances of practical interest. 1 Introduction The problem of assigning a set of facilities (with given flows between them) to a set of locations (with given distances between them) in such a way that the sum of the product between flows and distances is minimized is known as the facilities location problem [1] or the quadratic assignment ...
Memetic Algorithms and the Fitness Landscape of the Graph BiPartitioning Problem
 in Proceedings of the 5th International Conference on Parallel Problem Solving from Nature  PPSN
, 1998
"... . In this paper, two types of fitness landscapes of the graph bipartitioning problem are analyzed, and a memetic algorithm  a genetic algorithm incorporating local search  that finds nearoptimum solutions efficiently is presented. A search space analysis reveals that the fitness landscapes of g ..."
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Cited by 31 (6 self)
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. In this paper, two types of fitness landscapes of the graph bipartitioning problem are analyzed, and a memetic algorithm  a genetic algorithm incorporating local search  that finds nearoptimum solutions efficiently is presented. A search space analysis reveals that the fitness landscapes of geometric and nongeometric random graphs differ significantly, and within each type of graph there are also differences with respect to the epistasis of the problem instances. As suggested by the analysis, the performance of the proposed memetic algorithm based on KernighanLin local search is better on problem instances with high epistasis than with low epistasis. Further analytical results indicate that a combination of a recently proposed greedy heuristic and KernighanLin local search is likely to perform well on geometric graphs. The experimental results obtained for nongeometric graphs show that the proposed memetic algorithm (MA) is superior to any other heuristic known to us. For th...