Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
SATenstein: automatically building local search SAT solvers from components
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
A two-phase local search for the biobjective traveling salesman problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
A gender-based genetic algorithm for the automatic configuration of algorithms
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Two-phase Pareto local search for the biobjective traveling salesman problem
Journal of Heuristics
Automatic configuration of multi-objective ACO algorithms
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Parameter tuning boosts performance of variation operators in multiobjective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Adaptive "Anytime" two-phase local search
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
Automated configuration of mixed integer programming solvers
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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
Automatic (offline) configuration of algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Region based memetic algorithm for real-parameter optimisation
Information Sciences: an International Journal
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The automatic configuration of algorithms is a dynamic field of research. Its potential for producing highly performing algorithms may change the way we design algorithms. So far, automatic algorithm configuration tools have almost exclusively been applied to configure single-objective algorithms. In this paper, we investigate the usage of automatic algorithm configuration tools to improve multi-objective algorithms. In fact, this is the first article we are aware of where state-of-the-art multi-objective optimizers are configured in an automatic way. This automatic configuration is done for five variants of multi-objective flow-shop problems. Our experimental results show that we can reach at least the same and often a better final quality than a recently proposed state-of-the-art algorithm for these problems.