SIAM Journal on Applied Mathematics
Epidemic algorithms for replicated database maintenance
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Journal of Global Optimization
A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Grid Computing
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Gossip-based aggregation in large dynamic networks
ACM Transactions on Computer Systems (TOCS)
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
ACM Transactions on Computer Systems (TOCS)
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Distributed choice function hyper-heuristics for timetabling and scheduling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
T-Man: gossip-based overlay topology management
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
Validating evolutionary algorithms on volunteer computing grids
DAIS'10 Proceedings of the 10th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
Is the meta-EA a viable optimization method?
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance than any of the original heuristics. While HHs lend themselves naturally for distributed deployment, relatively little attention has been paid so far on the design and evaluation of distributed HHs. To our knowledge, our work is the first to present a detailed evaluation and comparison of distributed HHs for real parameter optimization in an island model. Our set of test functions includes well-known benchmark functions and two realistic space-probe trajectory optimization problems. The set of algorithms available to the HHs include several variants of differential evolution, and uniform random search. Our main conclusion is that some of the simplest HHs are surprisingly successful in a distributed environment, and the best HHs we tested provide a robust and stable good performance over a wide range of scenarios and parameters.