Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Real-world applications of Bayesian networks
Communications of the ACM
A graph-constructive approach to solving systems of geometric constraints
ACM Transactions on Graphics (TOG)
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Sketch-based pruning of a solution space within a formal geometric constraint solver
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
User profiling with Case-Based Reasoning and Bayesian Networks
International Joint Conference, 7th Ibero-American Conference, 15th Brazilian Symposium on AI, IBERAMIA-SBIA 2000, Open Discussion Track Proceedings on AI
Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks
Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Gradient-Based Optimization of Hyperparameters
Neural Computation
A model for parameter setting based on Bayesian networks
Engineering Applications of Artificial Intelligence
Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study
Expert Systems with Applications: An International Journal
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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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.