Estimation-based metaheuristics for the probabilistic traveling salesman problem
Computers and Operations Research
Modern continuous optimization algorithms for tuning real and integer algorithm parameters
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Off-line vs. on-line tuning: a study on MAX–MIN ant system for the TSP
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
MADS/F-race: mesh adaptive direct search meets F-race
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
A self-adaptive local search algorithm for the classical vehicle routing problem
Expert Systems with Applications: An International Journal
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Large neighbourhood search algorithms for the founder sequence reconstruction problem
Computers and Operations Research
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Computers and Industrial Engineering
International Journal of Applied Metaheuristic Computing
International Journal of Applied Metaheuristic Computing
International Journal of Applied Metaheuristic Computing
An analysis of post-selection in automatic configuration
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Automatic (offline) configuration of algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A survey on optimization metaheuristics
Information Sciences: an International Journal
Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem
Applied Soft Computing
Region based memetic algorithm for real-parameter optimisation
Information Sciences: an International Journal
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The importance of tuning metaheuristics is widely acknowledged in scientific literature. However, there is very little dedicated research on the subject. Typically, scientists and practitioners tune metaheuristics by hand, guided only by their experience and by some rules of thumb. Tuning metaheuristics is often considered to be more of an art than a science. This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning. By adopting a machine learning perspective, the author gives a formal definition of the tuning problem, develops a generic algorithm for tuning metaheuristics, and defines an appropriate experimental methodology for assessing the performance of metaheuristics.