Bayesian Genetic Programming

author = "Byoung-Tak Zhang",

title = "Bayesian Genetic Programming",

booktitle = "Foundations of Genetic Programming",

year = "1999",

editor = "Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca",

pages = "68--70",

address = "Orlando, Florida, USA",

month = "13 " # jul,

keywords = "genetic algorithms, genetic programming",

URL = "

http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/fogp99zhang.ps.gz",

URL = "

http://bi.snu.ac.kr/Publications/Conferences/International/FOGP99.ps",

URL = "

http://citeseer.ist.psu.edu/455364.html",

size = "3 pages",

abstract = "Introduction Genetic programming (GP) provides a powerful tool for learning models of some unknown process from specific fitness cases. Models are typically represented as tree-structured programs, and thus GP has a wide range of application domains. However, the general applicability of GP suffers from large amount of space and time required for generating intermediate solutions.",

notes = "GECCO'99 WKSHOP, part of \cite{haynes:1999:fogp}",