A Review of Major Application Areas of Differential Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "V. P. Plagianakos and D. K. Tasoulis and 
                 M. N. Vrahatis",
  title =        "A Review of Major Application Areas of Differential
  booktitle =    "Advances in Differential Evolution",
  publisher =    "Springer",
  year =         "2008",
  editor =       "Uday K. Chakraborty",
  volume =       "143",
  series =       "Studies in Computational Intelligence",
  pages =        "197--238",
  keywords =     "genetic algorithms, genetic programming, evolution
  language =     "English",
  isbn13 =       "978-3-540-68827-3",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  broken =       "http://www.math.upatras.gr/~vrahatis/PAPERS/CHAPTERS/PlagianakosTV08_Studies_Comput_Intell.pdf",
  DOI =          "doi:10.1007/978-3-540-68830-3_8",
  size =         "42 pages",
  abstract =     "In this chapter we present an overview of the major
                 applications areas of differential evolution. In
                 particular we pronounce the strengths of DE algorithms
                 in tackling many difficult problems from diverse
                 scientific areas, including single and multiobjective
                 function optimisation, neural network training,
                 clustering, and real life DNA microarray
                 classification. To improve the speed and performance of
                 the algorithm we employ distributed computing
                 architectures and demonstrate how parallel,
                 multi-population DE architectures can be used in single
                 and multiobjective optimisation. Using data mining we
                 present a methodology that allows the simultaneous
                 discovery of multiple local and global minimisers of an
                 objective function. At a next step we present
                 applications of DE in real life problems including the
                 training of integer weight neural networks and the
                 selection of genes of DNA microarrays in order to boost
                 predictive accuracy of classification models. The
                 chapter concludes with a discussion on promising future
                 extensions of the algorithm, and presents novel
                 mutation operators, that are the result of a genetic
                 programming procedure, as very interesting future
                 research direction.",

Genetic Programming entries for V P Plagianakos Dimitris K Tasoulis Michael N Vrahatis