A GPU-based implementation of an enhanced GEP algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Shao:2012:GECCO,

• author = "Shuai Shao and Xiyang Liu and Mingyuan Zhou and Jiguo Zhan and Xin Liu and Yanli Chu and Hao Chen",
• title = "A {GPU}-based implementation of an enhanced {GEP} algorithm",
• 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 = "999--1006",
• month = "7-11 " # jul,
• address = "Philadelphia, Pennsylvania, USA",
• organisation = "SIGEVO",
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming, Gene expression programming, pGEP, ET-tree, MLS, GPU, CUDA, SIMD, parallel evolutionary systems, NVIDIA Tesla C2050",
• DOI = "doi:10.1145/2330163.2330302",
• publisher_address = "New York, NY, USA",
• size = "8 pages",
• abstract = "Gene expression programming (GEP) is a functional genotype/phenotype system. The separation scheme increases the efficiency and reliability of GEP. However, the computational cost increases considerably with the expansion of the scale of problems. In this paper, we introduce a GPU-accelerated hybrid variant of GEP named pGEP (parallel GEP). In order to find the optimal constant coefficients locally on the fixed function structure, the Method of Least Square (MLS) has been embedded into the GEP evolutionary process. We tested pGEP using a broad problem set with a varying number of instances. In the performance experiment, the GPU-based GEP, when compared with the traditional GEP version, increased speeds by approximately 250 times. We compared pGEP with other well-known constant creation methods in terms of accuracy, demonstrating MLS performs at several orders of magnitude higher in terms of both the best residuals and average residuals.",
• notes = "Also known as \cite{2330302} 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 Shuai Shao Xiyang Liu Mingyuan Zhou Jiguo Zhan Xin Liu Yanli Chu Hao Chen

Citations