A Review of Major Application Areas of Differential Evolution

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

@InCollection{Plagianakos:2008:AiDE,

• author = "V. P. Plagianakos and D. K. Tasoulis and M. N. Vrahatis",
• title = "A Review of Major Application Areas of Differential Evolution",
• 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 strategies",
• 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:10.1.1.301.4869",
• 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=10.1.1.301.4869",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.301.4869.pdf",
• 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

Citations