Future Generation Computer Systems
Analysis of Generalized Pattern Searches
SIAM Journal on Optimization
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
SIAM Journal on Optimization
Finding Optimal Algorithmic Parameters Using Derivative-Free Optimization
SIAM Journal on Optimization
Tuning Metaheuristics: A Machine Learning Perspective
Tuning Metaheuristics: A Machine Learning Perspective
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Modern continuous optimization algorithms for tuning real and integer algorithm parameters
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
An analysis of post-selection in automatic configuration
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Finding appropriate parameter settings of parameterized algorithms or AI systems is an ubiquitous task in many practical applications. This task is usually tedious and time-consuming. To reduce human intervention, the study of methods for automated algorithm configuration has received increasing attention in recent years. In this article, we study themesh adaptive direct search (MADS) method for the configuration of parameterized algorithms. MADS is a direct search method for continuous, global optimization. For handling the stochasticity involved in evaluating the algorithm to be configured, we hybridized MADS with F-Race, a racing method that adaptively allocates an appropriate number of evaluations to each member of a population of candidate algorithm configurations. We experimentally study this hybrid of MADS and F-Race (MADS/F-Race) and compare it to other ways of defining the number of evaluations of each candidate configuration and to another method called I/F-Race. This comparison confirms the good performance and robustness of MADS/F-Race.