Treffer: Evolving a Statistics Class Using Object Oriented Evolutionary Programming.
Title:
Evolving a Statistics Class Using Object Oriented Evolutionary Programming.
Authors:
Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Ebner, Marc ebner@informatik.uni-wuerzburg.de, O'Neill, Michael m.oneill@ucd.ie, Ekárt, Anikó ekarta@aston.ac.uk, Vanneschi, Leonardo vanneschi@disco.unimib.it, Esparcia-Alcázar, Anna Isabel anna@iti.upv.es
Source:
Genetic Programming (9783540716020). 2007, p291-300. 10p.
Database:
Supplemental Index
Weitere Informationen
Object Oriented Evolutionary Programming is used to evolve programs that calculate some statistical measures on a set of numbers. We compared this technique with a more standard functional representation. We also studied the effects of scalar and Pareto-based multi-objective fitness functions to the induction of multi-task programs. We found that the induction of a program residing in an OO representation space is more efficient, yielding less fitness evaluations, and that scalar fitness performed better than Pareto-based fitness in this problem domain. [ABSTRACT FROM AUTHOR]