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17
A graphbased hyperheuristic for educational timetabling problems
 European Journal of Operational Research
, 2007
"... This paper presents an investigation of a simple generic hyperheuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to search for permutations of graph heuristics whic ..."
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Cited by 70 (24 self)
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This paper presents an investigation of a simple generic hyperheuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multistage hyperheuristic where the Tabu Search employs permutations upon a different number of graph heuristics in two stages. We study this graphbased hyperheuristic approach within the context of exploring fundamental issues concerning the search space of the hyperheuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyperheuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the finetuned bespoke stateoftheart approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyperheuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems. 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 38 (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.
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 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.
Examination Timetabling Using Late Acceptance Hyperheuristics
"... Abstract — A hyperheuristic is a high level problem solving methodology that performs a search over the space generated by a set of low level heuristics. One of the hyperheuristic frameworks is based on a single point search containing two main stages: heuristic selection and move acceptance. Most o ..."
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Cited by 14 (9 self)
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Abstract — A hyperheuristic is a high level problem solving methodology that performs a search over the space generated by a set of low level heuristics. One of the hyperheuristic frameworks is based on a single point search containing two main stages: heuristic selection and move acceptance. Most of the existing move acceptance methods compare a new solution, generated after applying a heuristic, against a current solution in order to decide whether to reject it or replace the current one. Late Acceptance Strategy is presented as a promising local search methodology based on a novel move acceptance mechanism. This method performs a comparison between the new candidate solution and a previous solution that is generated L steps earlier. In this study, the performance of a set of hyperheuristics utilising different heuristic selection methods combined with the Late Acceptance Strategy are investigated over an examination timetabling problem. The results illustrate the potential of this approach as a hyperheuristic component. The hyperheuristic formed by combining a random heuristic selection with Late Acceptance Strategy improves on the best results obtained in a previous study. I.
A 0/1 Integer Programming Model for the Office Space Allocation Problem
"... We propose a 0/1 integer programming model to tackle the office space allocation (OSA) problem which refers to assigning room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional requirements. In the propos ..."
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Cited by 2 (1 self)
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We propose a 0/1 integer programming model to tackle the office space allocation (OSA) problem which refers to assigning room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional requirements. In the proposed approach, these requirements can be modelled as constraints (hard constraints) or as objectives (soft constraints). Then, we conduct some experiments on benchmark instances and observe that setting certain constraints as hard (actual constraints) or soft (objectives) has a significant impact on the computational difficulty on this combinatorial optimisation problem.
A New Biological Operator in Genetic Algorithm for Class Scheduling Problem
"... This paper describes an innovative approach to solve Class Scheduling problem which is a constraint combinatorial NP hard problem. From the wonders of natural evolution, an important phenomenon of RNA interference induced silencing complex (RISC) can be used as Interference Induced Silencing operato ..."
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This paper describes an innovative approach to solve Class Scheduling problem which is a constraint combinatorial NP hard problem. From the wonders of natural evolution, an important phenomenon of RNA interference induced silencing complex (RISC) can be used as Interference Induced Silencing operator and it is incorporated into the Genetic Algorithm to solve any practical problems like Class Scheduling problem. The aim of this research is to create an automated system for class scheduling problem using Genetic Algorithm to the extent by a new biologically inspired operator, Interference Induced Silencing (IIS) operator that it can be used to set the instant specific preferences to generate the effective time table with the probabilistic operators like crossover and mutation. The framework of the fitness function has considered the hard constraints and the soft constraints. The results were proved to be efficient than the simple Genetic algorithm.
A Multilevel Cooperative Tabu Search Algorithm for the Covering Design Problem
"... We propose a multilevel cooperative search algorithm to compute upper bounds for Cλ(v,k,t), the minimum number of blocks in a t − (v,k,λ) covering design. Multilevel cooperative search is a search heuristic combining cooperative search and multilevel search. We first introduce a coarsening strategy ..."
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We propose a multilevel cooperative search algorithm to compute upper bounds for Cλ(v,k,t), the minimum number of blocks in a t − (v,k,λ) covering design. Multilevel cooperative search is a search heuristic combining cooperative search and multilevel search. We first introduce a coarsening strategy for the covering design problem which defines reduced forms of an original t − (v,k,λ) problem for each level of the multilevel search. A new tabu search algorithm is introduced to optimize the problem at each level. Cooperation operators between tabu search procedures at different levels include new recoarsening and interpolation operators. We report the results of tests that have been conducted on 158 covering design problems. Improved upper bounds have been found for 34 problems, many of which exhibit a tight gap between best known lower and upper bounds. The proposed heuristic appears to be a very promising approach to tackle other similar optimization problems in the field of combinatorial design.
A Grouping HyperHeuristic Framework: Application on Graph Colouring
"... Abstract Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimized. Selection hyperheuristics are high level general purpose search methodologies that operate on ..."
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Abstract Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimized. Selection hyperheuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than solutions. Most of the recently proposed selection hyperheuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance. At each step, a new solution is produced after a selected heuristic is applied to the solution in hand and then the move acceptance method is used to decide whether the resultant solution replaces the current one or not. In this study, we present a selection hyperheuristic framework including a fixed set of low level heuristics specifically designed for grouping problems. The performance of different hyperheuristics using different components within the framework is investigated on a representative grouping problem of graph colouring. Additionally, the hyperheuristic performing the best on graph colouring is applied to a benchmark of examination timetabling instances. The empirical results shows that the proposed grouping hyperheuristic is not only sufficiently general, but also able to achieve high quality solutions for graph colouring and examination timetabling.