In Multilevel Optimization there is usually a choice to be made between different models when carrying out design evaluations. The choice is between accurate / computationally expensive evaluations and approximate/ computational cheap ones. Here, a strategy is sought for selecting between different models during the search. The focus of the paper is on preliminary work carried out using a self organizing map (SOM) for model selectio
International audienceIn this paper, we present a new heuristic measure for optimizing database used...
-tness function, thereby treating model selection as an optimisation problem. However, it is unreali...
International audienceIn this paper, we tackle the problem of model selection when misclassification...
Variable selection is an important task in machine learning and data mining applications. In many re...
In many design optimization problems, the designer is faced with the dilemma of how to simulate the ...
The paper deals with supervised learning. In many problems, the training data contains only the fina...
In many problems in science and engineering, there are often a number of computational models that c...
Automatic model search procedures aim at identifying the model that maximises a given fitness functi...
Abstract Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in da...
People from a variety of industrial domains are beginning to realise that appropriate use of machine...
In this article, a new population-based algorithm for real-parameter global optimization is presente...
International audienceEvolutionary algorithms (EA) (Rechenberg, 1965) belong to a family of stochast...
Multiple Self-Organizing Maps (MSOMs) based classification methods are able to combine the advantage...
In many problems in science and engineering, it is often the case that there exist a number of compu...
Support vector machines are relatively new approach for creating classifiers that have become increa...
International audienceIn this paper, we present a new heuristic measure for optimizing database used...
-tness function, thereby treating model selection as an optimisation problem. However, it is unreali...
International audienceIn this paper, we tackle the problem of model selection when misclassification...
Variable selection is an important task in machine learning and data mining applications. In many re...
In many design optimization problems, the designer is faced with the dilemma of how to simulate the ...
The paper deals with supervised learning. In many problems, the training data contains only the fina...
In many problems in science and engineering, there are often a number of computational models that c...
Automatic model search procedures aim at identifying the model that maximises a given fitness functi...
Abstract Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in da...
People from a variety of industrial domains are beginning to realise that appropriate use of machine...
In this article, a new population-based algorithm for real-parameter global optimization is presente...
International audienceEvolutionary algorithms (EA) (Rechenberg, 1965) belong to a family of stochast...
Multiple Self-Organizing Maps (MSOMs) based classification methods are able to combine the advantage...
In many problems in science and engineering, it is often the case that there exist a number of compu...
Support vector machines are relatively new approach for creating classifiers that have become increa...
International audienceIn this paper, we present a new heuristic measure for optimizing database used...
-tness function, thereby treating model selection as an optimisation problem. However, it is unreali...
International audienceIn this paper, we tackle the problem of model selection when misclassification...