MADS/F-Race: Mesh Adaptive Direct Search Meets F-Race

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.

[1]  Thomas Bartz-Beielstein,et al.  Experimental Methods for the Analysis of Optimization Algorithms , 2010 .

[2]  Charles Audet,et al.  Analysis of Generalized Pattern Searches , 2000, SIAM J. Optim..

[3]  Mauro Birattari,et al.  Tuning Metaheuristics - A Machine Learning Perspective , 2009, Studies in Computational Intelligence.

[4]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[5]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[6]  CHARLES AUDET,et al.  Finding Optimal Algorithmic Parameters Using Derivative-Free Optimization , 2006, SIAM J. Optim..

[8]  Thomas Stützle,et al.  A Racing Algorithm for Configuring Metaheuristics , 2002, GECCO.

[9]  Charles Audet,et al.  Mesh Adaptive Direct Search Algorithms for Constrained Optimization , 2006, SIAM J. Optim..

[10]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[11]  Thomas Stützle,et al.  Iterated local search for the quadratic assignment problem , 2006, Eur. J. Oper. Res..

[12]  Zbigniew Michalewicz,et al.  Parameter Setting in Evolutionary Algorithms , 2007, Studies in Computational Intelligence.

[13]  Marcus Gallagher,et al.  Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks , 2007, Parameter Setting in Evolutionary Algorithms.

[14]  A. E. Eiben,et al.  Comparing parameter tuning methods for evolutionary algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[15]  Thomas Stützle,et al.  F-Race and Iterated F-Race: An Overview , 2010, Experimental Methods for the Analysis of Optimization Algorithms.