Experimental evaluation of an automatic parameter setting system

Finding the parameter setting that will result in the optimal performance of a given algorithm for solving a problem is a tedious task. This paper briefly describes a system that automatically chooses the best algorithm parameter configuration conditioned by the current problem instance to solve. Th...

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Bibliographic Details
Published in:Expert systems with applications Vol. 37; no. 7; pp. 5224 - 5238
Main Authors: Pavón, R., Díaz, F., Laza, R., Luzón, M.V.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2010
Subjects:
Algorithms
Bayesian networks
Boron nitride
Case-based reasoning
Constructive geometric constraint solving
Expert systems
Genetic algorithms
Mathematical models
Networks
Optimization
Setting parameters
Tasks
Setting parameters
Bayesian networks
Constructive geometric constraint solving
Case-based reasoning
Genetic algorithms
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:Finding the parameter setting that will result in the optimal performance of a given algorithm for solving a problem is a tedious task. This paper briefly describes a system that automatically chooses the best algorithm parameter configuration conditioned by the current problem instance to solve. The system uses bayesian networks (BN) and case-based reasoning (CBR) methodology to find such a configuration. CBR provides a mechanism to acquire knowledge about the specific problem domain. BN provide a tool to model quantitative and qualitative relationships between parameters of interest. However, the aim of this work is to empirically evaluate the system described, using as an example the configuration of a genetic algorithm that solves the root identification problem. In this context, we report on several statistically guided experimental evaluations. The experimental results confirm the validity of the proposed system and its potential effectiveness for configuring algorithms.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2009.12.087