DMCA
A Review of Major Application Areas of Differential Evolution
BibTeX
@MISC{Plagianakos_areview,
author = {V. P. Plagianakos and D. K. Tasoulis and M. N. Vrahatis},
title = {A Review of Major Application Areas of Differential Evolution},
year = {}
}
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Abstract
Summary. 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 optimization, 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 utilised in single and multiobjective optimization. Using data mining we present a methodology that allows the simultaneous discovery of multiple local and global minimizers 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. 1
Keyphrases
major application area differential evolution integer weight neural network dna microarrays data mining genetic programming procedure objective function real life dna microarray classification present application present novel mutation operator promising future extension many difficult problem diverse scientific area next step classification model predictive accuracy multi population de architecture neural network training multiobjective function optimization global minimizers simultaneous discovery multiobjective optimization real life problem interesting future research direction computing architecture
