A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming

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

@TechReport{vuw-CS-TR-06-3,

• author = "Huayang Xie and Mengjie Zhang and Peter Andreae",
• title = "A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming",
• institution = "Computer Science, Victoria University of Wellington",
• year = "2006",
• number = "CS-TR-06-3",
• address = "New Zealand",
• keywords = "genetic algorithms, genetic programming, Fitness evaluation, good predecessor programs, population clustering",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-3.abs.html",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-3.pdf",
• abstract = "Good Predecessor Programs (GPPs) are the ancestors of the best program found in a Genetic Programming (GP) evolution. This paper reports on an investigation into GPPs with the ultimate goal of reducing fitness evaluation cost in tree-based GP systems. A framework is developed for gathering information about GPPs and a series of experiments is conducted on a symbolic regression problem, a binary classification problem, and a multi-class classification program with increasing levels of difficulty in different domains. The analysis of the data shows that during evolution, GPPs typically constitute between less than 33per cent of the total programs evaluated, and may constitute less than 5per cent. The analysis results further shows that in all evaluated programs, the proportion of GPPs is reduced by increasing tournament size and to a less extent, affected by population size. Problem difficulty seems to have no clear influence on the proportion of GPPs.",

}

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang Peter Andreae

Citations