2 citations · 6 across the 8 of their papers we have counts for
5 papers · 1 filter
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts
Lukáš Korel, Alexander S. Behr, Norbert Kockmann +1
This paper provides an insight into the possibility of how to find ontologies most relevant to scientific texts using artificial neural networks. The basic idea of the presented ap…
Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context
Zbyněk Pitra, Jan Koza, Jiří Tumpach +1
Surrogate modeling has become a valuable technique for black-box optimization tasks with expensive evaluation of the objective function. In this paper, we investigate the relations…
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models
Jakub Repicky, Lukas Bajer, Zbynek Pitra +1
The interest in accelerating black-box optimizers has resulted in several surrogate model-assisted version of the Covariance Matrix Adaptation Evolution Strategy, a state-of-the-ar…
Model Guided Sampling Optimization for Low-dimensional Problems
Lukas Bajer, Martin Holena
Optimization of very expensive black-box functions requires utilization of maximum information gathered by the process of optimization. Model Guided Sampling Optimization (MGSO) fo…
Two Gaussian Approaches to Black-Box Optomization
Lukáš Bajer, Martin Holeňa
Outline of several strategies for using Gaussian processes as surrogate models for the covariance matrix adaptation evolution strategy (CMA-ES).