Evolutionary Algorithms for Multisensor Data Fusion

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Author(s): Jianhua Yang (University of Warwick, UK), Evor L. Hines (University of Warwick, UK), John E. Sloper (University of Warwick, UK), D. D. Iliescu (University of Warwick, UK)and Mark S. Leeson (University of Warwick, UK)
Copyright: 2011
Pages: 16
Source title: Intelligent Systems for Machine Olfaction: Tools and Methodologies
Source Author(s)/Editor(s): Evor L. Hines (University of Warwick, UK)and Mark S. Leeson (University of Warwick, UK)
DOI: 10.4018/978-1-61520-915-6.ch002

Keywords: Artificial Intelligence / Biologically-Inspired Computing / Engineering Science Reference / Medicine & Healthcare

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Abstract

The aim of Multisensor Data Fusion (MDF) is to eliminate redundant, noisy or irrelevant information and thus find an optimal subset from an array of high dimensionality. An important feature of MDF is that the signals are constantly evolving instead of being static. This provides an opportunity for Evolutionary Computation (EC) algorithms to be developed to solve MDF tasks. This chapter describes the application of three EC algorithms to widely used datasets. Comparative studies were performed so that relative advantage and disadvantages of the different approaches could be investigated. From this study, authros found that ECs performed in the feature selection stage can greatly reduce the dataset dimensionality and hence enhance the MDF system performance; when being used in a way to represent knowledge, ECs can dramatically increase rules when input data are not clustered.

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