Parallel multi-objective Ant Programming for classification using GPUs

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

@Article{Cano:2013:JPDC,

• author = "Alberto Cano and Juan Luis Olmo and Sebastian Ventura",
• title = "Parallel multi-objective Ant Programming for classification using {GPUs}",
• journal = "Journal of Parallel and Distributed Computing",
• year = "2013",
• volume = "73",
• number = "6",
• pages = "713--728",
• month = jun,
• keywords = "genetic algorithms, genetic programming, GPU, Reverse Polish RPN, grammar based, Ant programming (AP), Ant colony optimisation (ACO), Parallel computing, Classification",
• ISSN = "0743-7315",
• DOI = "doi:10.1016/j.jpdc.2013.01.017",
• size = "16 pages",
• abstract = "Classification using Ant Programming is a challenging data mining task which demands a great deal of computational resources when handling data sets of high dimensionality. This paper presents a new parallelisation approach of an existing multi-objective Ant Programming model for classification, using GPUs and the nVidia CUDA programming model. The computational costs of the different steps of the algorithm are evaluated and it is discussed how best to parallelise them. The features of both the CPU parallel and GPU versions of the algorithm are presented. An experimental study is carried out to evaluate the performance and efficiency of the interpreter of the rules, and reports the execution times and speedups regarding variable population size, complexity of the rules mined and dimensionality of the data sets. Experiments measure the original single-threaded and the new multi-threaded CPU and GPU times with different number of GPU devices. The results are reported in terms of the number of Giga GP operations per second of the interpreter (up to 10 billion GPops/s) and the speedup achieved (up to 834 times vs CPU, 212 times vs 4-threaded CPU). The proposed GPU model is demonstrated to scale efficiently to larger datasets and to multiple GPU devices, which allows the expansion of its applicability to significantly more complicated data sets, previously unmanageable by the original algorithm in reasonable time.",
• notes = "genetic programming. GPU GPops/second given for interpreter only. Two nVidia GPUs (GTX 285, 480) per host PC. Ubuntu Linux CUDA 4.2. Occupancy. UCI poker, etc. Evolved decision rules. (One per output class?) Host parallel code uses Java threads. Rules in constant memory, stack in local (off-chip) memory (L1/L2 cache).",

}

Genetic Programming entries for Alberto Cano Rojas Juan Luis Olmo Sebastian Ventura

Citations