An investigation of local patterns for estimation of distribution genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Hemberg:2012:GECCO,

• author = "Erik Hemberg and Kalyan Veeramachaneni and James McDermott and Constantin Berzan and Una-May O'Reilly",
• title = "An investigation of local patterns for estimation of distribution genetic programming",
• booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference",
• year = "2012",
• editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
• isbn13 = "978-1-4503-1177-9",
• pages = "767--774",
• keywords = "genetic algorithms, genetic programming",
• month = "7-11 " # jul,
• organisation = "SIGEVO",
• address = "Philadelphia, Pennsylvania, USA",
• DOI = "doi:10.1145/2330163.2330270",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "We present an improved estimation of distribution (EDA) genetic programming (GP) algorithm which does not rely upon a prototype tree. Instead of using a prototype tree, Operator-Free Genetic Programming learns the distribution of ancestor node chains, {"}n-grams{"}, in a fit fraction of each generation's population. It then uses this information, via sampling, to create trees for the next generation. Ancestral n-grams are used because an analysis of a GP run conducted by learning depth first graphical models for each generation indicated their emergence as substructures of conditional dependence. We are able to show that our algorithm, without an operator and a prototype tree, achieves, on average, performance close to conventional tree based crossover GP on the problem we study. Our approach sets a direction for pattern-based EDA GP which off ers better tractability and improvements over GP with operators or EDAs using prototype trees.",
• notes = "Also known as \cite{2330270} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)",

}

Genetic Programming entries for Erik Hemberg Kalyan Veeramachaneni James McDermott Constantin Berzan Una-May O'Reilly

Citations