Parallel linear genetic programming for multi-class classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Downey:2012:GPEM,

• author = "Carlton Downey and Mengjie Zhang and Jing Liu",
• title = "Parallel linear genetic programming for multi-class classification",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2012",
• volume = "13",
• number = "3",
• pages = "275--304",
• month = sep,
• note = "Special issue on selected papers from the 2011 European conference on genetic programming",
• keywords = "genetic algorithms, genetic programming, Linear genetic programming, Classification, Parallel structure, Caching",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-012-9162-9",
• size = "30 pages",
• abstract = "Motivated by biological inspiration and the issue of instruction disruption, we develop a new form of Linear Genetic Programming (LGP) called Parallel LGP (PLGP) for classification problems. PLGP programs consist of multiple lists of instructions. These lists are executed in parallel after which the resulting vectors are combined to produce the classification result. PLGP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform regular LGP. Furthermore, PLGP programs are naturally suited to caching due to their parallel architecture. Although caching techniques have been used in tree based GP, to our knowledge, there are no caching techniques specifically developed for LGP. Thus, a novel caching technique is also developed with the intrinsic properties of PLGP in mind, which can decrease fitness evaluation time by almost an order of magnitude for the classification problems.",
• notes = "Jing Liu = http://see.xidian.edu.cn/faculty/liujing/ EuroGP 2011 \cite{Silva:2011:GP}",
• affiliation = "School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand",

}

Genetic Programming entries for Carlton Downey Mengjie Zhang Jing Liu

Citations