학술논문
Ordinal Pareto Genetic Programming
Document Type
Conference
Author
Source
2006 IEEE International Conference on Evolutionary Computation Evolutionary Computation, 2006. CEC 2006. IEEE Congress on. :3114-3120 2006
Subject
Language
ISSN
1089-778X
1941-0026
1941-0026
Abstract
This paper introduces the first attempt to combine the theory of ordinal optimization and symbolic regression via genetic programming. A new approach called Ordinal ParetoGP allows obtaining considerably fitter solutions with more consistency between independent runs while spending less computational effort. The conclusions are supported by a number of experiments using three symbolic regression benchmark problems of various size.