- author = "Yuliana Sarai {Martinez Ramos}",
- title = "Prediction Performance and Problem Difficulty in Genetic Programming",
- titletranslation = "Prediccion de rendimiento y dificultad de problemas en programacion genetica",
- school = "ITT, Instituto tecnologico de Tijuana",
- year = "2016",
- type = "Doctor in Engineering Sciences",
- address = "Tijuana, Baja California, Mexico",
- month = jun,
- keywords = "genetic algorithms, genetic programming, problem difficulty, prediction of expected performance, supervised learning",
- identifier = "tel-01668769",
- language = "en",
- oai = "oai:HAL:tel-01668769v1",
-
URL = "
https://hal.inria.fr/tel-01668769",
-
URL = "https://hal.inria.fr/tel-01668769/document",
-
URL = "
https://hal.inria.fr/tel-01668769/file/tesis_Yuliana.pdf",
-
URL = "https://tel.archives-ouvertes.fr/tel-01668769",
- size = "158 pages",
- abstract = "The estimation of problem difficulty is an open issue in Genetic Programming(GP). The goal of this work is to generate models that predict the expected performance of a GP-based classifier when it is applied to an unseen task. Classification problems are described 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 are referred to as predictors of expected performance (PEPs). We extend this approach by using an ensemble of specialized predictors (SPEP), dividing classification problems into groups and choosing the corresponding SPEP. The proposed predictors are trained using 2D synthetic classification problems with balanced datasets. The models are then used to predict the performance of the GP classifier on unseen real world datasets that are multidimensional and imbalanced. This work is the first to provide a performance prediction of a GP system on testdata, while previous works focused on predicting training performance. Accurate predictive models are generated by posing a symbolic regression task and solving it with GP. These results are achieved by using highly descriptive features and including a dimensionality reduction stage that simplifies the learning and testing process. The proposed approach could be extended to other classification algorithms and used as the basis of an expert system for algorithm selection.",
-
notes = "Supervisor: Leonardo Trujillo Reyes
Also known as \cite{oai:HAL:tel-01668769v1} Also known as \cite{DBLP:phd/hal/Martinez16a}",
