Crossover, Macromutationand, and Population-Based Search
Proceedings of the 6th International Conference on Genetic Algorithms
Recent Developments in Practical Course Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Case-based heuristic selection for timetabling problems
Journal of Scheduling
A general heuristic for vehicle routing problems
Computers and Operations Research
Evolutionary Computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
A Hyper-Heuristic Using GRASP with Path-Relinking: A Case Study of the Nurse Rostering Problem
Journal of Information Technology Research
A runtime analysis of simple hyper-heuristics: to mix or not to mix operators
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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Hyper-heuristics can be thought of as "heuristics to choose heuristics". They are concerned with adaptively finding solution methods, rather than directly producing a solution for the particular problem at hand. Hence, an important feature of hyper-heuristics is that they operate on a search space of heuristics rather than directly on a search space of problem solutions. A motivating aim is to build systems which are fundamentally more generic than is possible today. Understanding the structure of these heuristic search spaces is therefore, a research direction worth exploring. In this paper, we use the notion of fitness landscapes in the context of constructive hyper-heuristics. We conduct a landscape analysis on a heuristic search space conformed by sequences of graph coloring heuristics for timetabling. Our study reveals that these landscapes have a high level of neutrality and positional bias. Furthermore, although rugged, they have the encouraging feature of a globally convex or big valley structure, which indicates that an optimal solution would not be isolated but surrounded by many local minima. We suggest that using search methodologies that explicitly exploit these features may enhance the performance of constructive hyper-heuristics.