- author = "Alwyn V. Husselmann and K. A. Hawick",
- title = "Geometric Optimisation using {Karva} for Graphical Processing Units",
- booktitle = "Proc. 15th International Conference on Artificial Intelligence (ICAI'13)",
- year = "2013",
- editor = "Hamid R. Arabnia and David de la Fuente and Elena B. Kozerenko and Peter M. LaMonica and Raymond A. Liuzzi and Jose A. Olivas and Todd Waskiewicz",
- volume = "I",
- pages = "225--231",
- address = "Las Vegas, USA",
- month = "22-25 " # jul,
- publisher = "WorldComp",
- keywords = "genetic algorithms, genetic programming, gene expression programming, GPU, CUDA, geometric, parallel computing, SMIT, particle swarm, PSO, GPSO Santa Fe Ant Trail",
- timestamp = "2013.03.19",
- ISBN = "1-60132-246-1",
-
URL = "
https://www.researchgate.net/publication/266261192_Geometric_Optimisation_using_Karva_for_Graphical_Processing_Units",
-
URL = "
http://worldcomp-proceedings.com/proc/p2013/ICA2335.pdf",
- size = "7 pages",
- abstract = "Population-based evolutionary algorithms continue to play an important role in artificially intelligent systems, but can not always easily use parallel computation. We have combined a geometric (any-space) particle swarm optimisation algorithm with use of Ferreira Karva language of gene expression programming to produce a hybrid that can accelerate the genetic operators and which can rapidly attain a good solution. We show how Graphical Processing Units (GPUs) can be exploited for this. While the geometric particle swarm optimiser is not markedly faster that genetic programming, we show it does attain good solutions faster, which is important for the problems discussed when the fitness function is inordinately expensive to compute.",
-
notes = "See also technical Report: CSTN-191, Computer Science,
Massey University, Auckland, New
Zealand
http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=28299©ownerid=5571",
