Skip to Main content Skip to Navigation
Conference papers

Avoiding the Bloat with Stochastic Grammar-based Genetic Programming

Abstract : The application of Genetic Programming to the discovery of empirical laws is often impaired by the huge size of the search space, and consequently by the computer resources needed. In many cases, the extreme demand for memory and CPU is due to the massive growth of non-coding segments, the introns. The paper presents a new program evolution framework which combines distribution-based evolution in the PBIL spirit, with grammar-based genetic programming; the information is stored as a probability distribution on the gra mmar rules, rather than in a population. Experiments on a real-world like problem show that this approach gives a practical solution to the problem of intron growth.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/inria-00001095
Contributor : Marc Schoenauer Connect in order to contact the contributor
Submitted on : Sunday, February 5, 2006 - 9:37:24 AM
Last modification on : Wednesday, November 17, 2021 - 12:28:16 PM
Long-term archiving on: : Saturday, April 3, 2010 - 10:09:57 PM

Identifiers

Citation

Alain Ratle, Michèle Sebag. Avoiding the Bloat with Stochastic Grammar-based Genetic Programming. Evolution Artificielle 01, 2001, Le Creusot, France. pp.254-266. ⟨inria-00001095⟩

Share

Metrics

Record views

92

Files downloads

177