Adaptive iterated local search for cross-domain optimisation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
HyFlex: a benchmark framework for cross-domain heuristic search
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Hyper-Heuristic based on iterated local search driven by evolutionary algorithm
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
A non-adaptive stochastic local search algorithm for the CHeSC 2011 competition
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Vehicle routing and adaptive iterated local search within the hyflex hyper-heuristic framework
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
A hyper-heuristic inspired by pearl hunting
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Evaluation of a family of reinforcement learning cross-domain optimization heuristics
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
A new hyperheuristic algorithm for cross-domain search problems
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
An intelligent hyper-heuristic framework for CHeSC 2011
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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Hyper-heuristics comprise a set of approaches which are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was firstly used in 2000 to describe 'heuristics to choose heuristics' in the context of combinatorial optimization. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s and 1970s, and a number of related research areas can be identified. The definition of hyper-heuristics has been recently extended to refer to 'a search method or learning mechanism for selecting or generating heuristics to solve computational search problems'. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This tutorial will discuss the motivation, definition and classification of hyper-heuristics. The tutorial will also cover a recent international research competition: the first 'Cross-domain Heuristic Search Challenge'(CHeSC 2011) (http://www.asap.cs.nott.ac.uk/chesc2011). The challenge was to design a search algorithm that works well, not only across different instances of the same problem, but also across different problem domains. Using a sport metaphor, it is the 'Decathlon' challenge of search heuristics. To support the competition, and to promote research in this area, a software framework (HyFlex) was developed, which features a common interface for dealing with different optimization problems. The tutorial will summarize the main features of HyFlex and the competition; and will present an analysis of the results, highlighting the design principles of the most successful cross-domain hyper-heuristics.