Grammar-based genetic programming with dependence learning and bayesian network classifier

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

@InProceedings{Wong:2014:GECCO,

• author = "Pak-Kan Wong and Leung-Yau Lo and Man-Leung Wong and Kwong-Sak Leung",
• title = "Grammar-based genetic programming with dependence learning and bayesian network classifier",
• booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation",
• year = "2014",
• editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges",
• isbn13 = "978-1-4503-2662-9",
• pages = "959--966",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, Automatic Programming, grammar-based genetic programming, Bayesian network, classifier",
• URL = "http://doi.acm.org/10.1145/2576768.2598256",
• DOI = "doi:10.1145/2576768.2598256",
• size = "8 pages",
• abstract = "Grammar-Based Genetic Programming formalises constraints on the solution structure based on domain knowledge to reduce the search space and generate grammatically correct individuals. Nevertheless, building blocks in a program can often be dependent, so the effective search space can be further reduced. Approaches have been proposed to learn the dependence using probabilistic models and shown to be useful in finding the optimal solutions with complex structure. It raises questions on how to use the individuals in the population to uncover the underlying dependence. Usually, only the good individuals are selected. To model the dependence better, we introduce Grammar-Based Genetic Programming with Bayesian Network Classifier (GBGPBC) which also uses poorer individuals. With the introduction of class labels, we further propose a refinement technique on probability distribution based on class label. Our results show that GBGPBC performs well on two benchmark problems. These techniques boost the performance of our system.",
• notes = "Also known as \cite{2598256} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)",

}

Genetic Programming entries for Pak-Kan Wong "Peter" Leung-Yau Lo Man Leung Wong Kwong-Sak Leung

Citations