Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
A Hybrid Heuristic for the p-Median Problem
Journal of Heuristics
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An analysis of mutative σ-self-adaptation on linear fitness functions
Evolutionary Computation
An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Adaptive operator selection with dynamic multi-armed bandits
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Analyzing the landscape of a graph based hyper-heuristic for timetabling problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Towards the decathlon challenge of search heuristics
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
A new dispatching rule based genetic algorithm for the multi-objective job shop problem
Journal of Heuristics
Ant based hyper heuristics with space reduction: a case study of the p-median problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics
IEEE Transactions on Evolutionary Computation
Hill climbers and mutational heuristics in hyperheuristics
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Building hyper-heuristics through ant colony optimization for the 2d bin packing problem
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Automating the packing heuristic design process with genetic programming
Evolutionary Computation
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An analysis on separability for Memetic Computing automatic design
Information Sciences: an International Journal
Population based Local Search for university course timetabling problems
Applied Intelligence
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Recent years have witnessed the great success of hyper-heuristics applying to numerous real-world applications. Hyper-heuristics raise the generality of search methodologies by manipulating a set of low level heuristics (LLHs) to solve problems, and aim to automate the algorithm design process. However, those LLHs are usually parameterized, which may contradict the domain independent motivation of hyper-heuristics. In this paper, we show how to automatically maintain low level parameters (LLPs) using a hyper-heuristic with LLP adaptation (AD-HH), and exemplify the feasibility of AD-HH by adaptively maintaining the LLPs for two hyper-heuristic models. Furthermore, aiming at tackling the search space expansion due to the LLP adaptation, we apply a heuristic space reduction (SAR) mechanism to improve the AD-HH framework. The integration of the LLP adaptation and the SAR mechanism is able to explore the heuristic space more effectively and efficiently. To evaluate the performance of the proposed algorithms, we choose the p-median problem as a case study. The empirical results show that with the adaptation of the LLPs and the SAR mechanism, the proposed algorithms are able to achieve competitive results over the three heterogeneous classes of benchmark instances.