A particle swarm pattern search method for bound constrained global optimization
Journal of Global Optimization
Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search
Journal of Global Optimization
A Model-Based Learning Process for Modeling Coarticulation of Human Speech
IEICE - Transactions on Information and Systems
An experimental investigation of model-based parameter optimisation: SPO and beyond
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Time-bounded sequential parameter optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
How much do we "pay" for using default parameters?
Computational Optimization and Applications
Combined heat and power economic dispatch by mesh adaptive direct search algorithm
Expert Systems with Applications: An International Journal
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
Tradeoffs in the empirical evaluation of competing algorithm designs
Annals of Mathematics and Artificial Intelligence
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
Updating the regularization parameter in the adaptive cubic regularization algorithm
Computational Optimization and Applications
Automatic (offline) configuration of algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The objectives of this paper are twofold. We devise a general framework for identifying locally optimal algorithmic parameters. Algorithmic parameters are treated as decision variables in a problem for which no derivative knowledge or existence is assumed. A derivative-free method for optimization seeks to minimize some measure of performance of the algorithm being fine-tuned. This measure is treated as a black-box and may be chosen by the user. Examples are given in the text. The second objective is to illustrate this framework by specializing it to the identification of locally optimal trust-region parameters in unconstrained optimization. The derivative-free method chosen to guide the process is the mesh adaptive direct search, a generalization of pattern search methods. We illustrate the flexibility of the latter and in particular make provision for surrogate objectives. Locally, optimal parameters with respect to overall computational time on a set of test problems are identified. Each function call may take several hours and may not always return a predictable result. A tailored surrogate function is used to guide the search towards a local solution. The parameters thus identified differ from traditionally used values, and allow one to solve a problem that remained otherwise unsolved in a reasonable time using traditional values.