The study of problem difficulty is an open issue in Genetic Programming (GP). Thegoal of this work is to generate models that predict the expected performance of a GPbasedclassifier when it is applied to an unseen task. Classification problems aredescribed using domain-specific features, some of which are proposed in this work,and these features are given as input to the predictive models. These models arereferred to as predictors of expected performance (PEPs). We extend this approach byusing an ensemble of specialized predictors (SPEP), dividing classification problemsinto specified groups and choosing the corresponding SPEP. The proposed predictorsare trained using 2D synthetic classification problems with balanced datasets. Themodels ar...
Classification is one of the most researchable ideas in machine learning and data mining. A wide ran...
This document contains a selection of research works to which I have contributed. It is structured a...
International audienceRecent research advances on Tangled Program Graphs (TPGs) have demonstrated th...
The estimation of problem difficulty is an open issue in genetic programming (GP). The goal of this ...
The estimation of problem difficulty is an open issue in Genetic Programming(GP). The goal of this w...
During the development of applied systems, an important problem that must be addressed is that of ch...
During the development of applied systems, an important problem that must be addressed is that of ch...
In the field of Genetic Programming (GP) a question exists that is difficult to solve; how can probl...
An open question within Genetic Programming (GP) is how to characterize problem difficulty. The goal...
One of the main open problems within Genetic Programming (GP) is to meaningfully characterize the di...
Genetic Programming (GP) is a branch of Genetic Algorithms (GA) that searches for the best operatio...
La estimación de la dificultad de problemas es un tema abierto en Programación Genética (GP). El obj...
The quality of the evolved solutions of an evolutionary algorithm (EA) varies across different runs ...
This thesis introduces various machine learning algorithms which can be used in prediction tasks bas...
Classification is one of the most researchable ideas in machine learning and data mining. A wide ran...
This document contains a selection of research works to which I have contributed. It is structured a...
International audienceRecent research advances on Tangled Program Graphs (TPGs) have demonstrated th...
The estimation of problem difficulty is an open issue in genetic programming (GP). The goal of this ...
The estimation of problem difficulty is an open issue in Genetic Programming(GP). The goal of this w...
During the development of applied systems, an important problem that must be addressed is that of ch...
During the development of applied systems, an important problem that must be addressed is that of ch...
In the field of Genetic Programming (GP) a question exists that is difficult to solve; how can probl...
An open question within Genetic Programming (GP) is how to characterize problem difficulty. The goal...
One of the main open problems within Genetic Programming (GP) is to meaningfully characterize the di...
Genetic Programming (GP) is a branch of Genetic Algorithms (GA) that searches for the best operatio...
La estimación de la dificultad de problemas es un tema abierto en Programación Genética (GP). El obj...
The quality of the evolved solutions of an evolutionary algorithm (EA) varies across different runs ...
This thesis introduces various machine learning algorithms which can be used in prediction tasks bas...
Classification is one of the most researchable ideas in machine learning and data mining. A wide ran...
This document contains a selection of research works to which I have contributed. It is structured a...
International audienceRecent research advances on Tangled Program Graphs (TPGs) have demonstrated th...