Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
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
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Proceedings of the 6th International Conference on Genetic Algorithms
Gradient-Based Optimization of Hyperparameters
Neural Computation
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Statistical analysis of the main parameters involved in the designof a genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study
Expert Systems with Applications: An International Journal
Experimental evaluation of an automatic parameter setting system
Expert Systems with Applications: An International Journal
Low-cost model selection for SVMs using local features
Engineering Applications of Artificial Intelligence
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One of the difficulties that the user faces when using a model to solve a problem is that, before running the model, a set of parameter values have to be specified. Deciding on an appropriate set of parameter values is not an easy task. Over the years, several standard optimization methods, as well as various alternative approaches according to the problem at hand, have been proposed for parameter setting. These techniques have their merits and demerits, but usually they have a fairly restricted application range, including a lack of generality or the need of user supervision. This paper proposes a meta-model that generates the recommendations about the best parameter values for the model of interest. Its main characteristic is that it is an automatic meta-model that can be applied to any model. For evaluation purposes and in order to be able to compare our results with results obtained by others, a real geometric problem was selected. The experiments show the validity of the proposed adjustment model.