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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Barrero, D.F. R-Moreno, M.D. Castano, B. Camacho, D. |
| Copyright Year | 2011 |
| Description | Author affiliation: Departamento de Matemáticas, Universidad de Alcalá, Crta. Madrid-Barcelona Km. 33,6, Alcala de Henares, Madrid, Spain (Castano, B.) || Departamento de Informática, Universidad Autónoma de Madrid, C/ Francisco Tomas y Valiente 11, Madrid, Spain (Camacho, D.) || Departamento de Automática, Universidad de Alcalá, Crta. Madrid-Barcelona Km. 33,6, Alcala de Henares, Madrid, Spain (Barrero, D.F.; R-Moreno, M.D.) |
| Abstract | Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error. |
| Starting Page | 1164 |
| Ending Page | 1171 |
| File Size | 827514 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424478347 |
| e-ISBN | 9781424478354 |
| DOI | 10.1109/CEC.2011.5949748 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Estimation Accuracy Histograms Measurement uncertainty Iron Electronic mail |
| Content Type | Text |
| Resource Type | Article |
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