National Repository of Grey Literature 3 records found  Search took 0.00 seconds. 
Accelerating evolutionary algorithms by decision trees and their generalizations
Klíma, Jan ; Holeňa, Martin (advisor) ; Hauzar, David (referee)
Evolutionary algorithms are one of the most successful methods for solving non-traditional optimization problems. As they employ only function values of the objective function, evolutionary algorithms converge much more slowly than optimization methods for smooth functions. This property of evolutionary algorithms is particularly disadvantageous in the context of costly and time-consuming empirical way of obtaining values of the objective function. However, evolutionary algorithms can be substantially speeded up by employing a sufficiently accurate regression model of the empirical objective function. This thesis provides a survey of utilizability of regression trees and their ensembles as a surrogate model to accelerate convergence of evolutionary optimization.
Classification and Regression Trees in R
Nemčíková, Lucia ; Bašta, Milan (advisor) ; Vilikus, Ondřej (referee)
Tree-based methods are a nice add-on to traditional statistical methods when solving classification and regression problems. The aim of this master thesis is not to judge which approach is better but rather bring the overview of these methods and apply them on the real data using R. Focus is made especially on the basic methodology of tree-based models and the application in specific software in order to provide wide range of tool for reader to be able to use these methods. One part of the thesis touches the advanced tree-based methods to provide full picture of possibilities.

Interested in being notified about new results for this query?
Subscribe to the RSS feed.