Evolving a Statistics Class Using Object Oriented Evolutionary Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{eurogp07:agapitos1,

• author = "Alexandros Agapitos and Simon M. Lucas",
• title = "Evolving a Statistics Class Using Object Oriented Evolutionary Programming",
• editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
• booktitle = "Proceedings of the 10th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "4445",
• year = "2007",
• address = "Valencia, Spain",
• month = "11-13 " # apr,
• pages = "291--300",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-71602-0",
• ISBN = "3-540-71602-5",
• DOI = "doi:10.1007/978-3-540-71605-1_27",
• abstract = "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.",
• notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in conjunction with EvoCOP2007, EvoBIO2007 and EvoWorkshops2007",

}

Genetic Programming entries for Alexandros Agapitos Simon M Lucas

Citations