Dominance-Based Pareto-Surrogate for Multi-Objective Optimization
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In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during th ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...
This paper discusses the idea of using a single Pareto-compliant surrogate model for multiobjective optimization. While most surrogate approaches to multi-objective optimization build a surrogate model for each objective, the recently proposed mono surroga ...
This paper applies a novel two-layer optimizing control scheme to a kite-control benchmark problem. The upper layer is a recent real-time optimization algorithm, called Directional Modifier Adaptation, which represents a variation of the popular Modifier A ...
We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resulting optimization problems are eq ...
The design of a microvascular flow network embedded in an actively-cooled polymeric material is presented. A multi-objective Genetic Algorithm (GA) combined with the finite element method is first used to determine the quasi-optimized network configuration ...
Machine learning is most often cast as an optimization problem. Ideally, one expects a convex objective function to rely on efficient convex optimizers with nice guarantees such as no local optima. Yet, non-convexity is very frequent in practice and it may ...
Evaluative techniques offer a tremendous potential for on-line controller design. However, when the optimization space is large and the performance metric is noisy, the time needed to properly evaluate candidate solutions becomes prohibitively large and, a ...
Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classif ...