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Analysis of cancer data using evolutionary computation
ADFA School of Information Technology and Electrical Engineering, The University of New South Wales, Canberra, ACT, Australia .
School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.ORCID iD: 0000-0002-4255-5130
2009 (English)In: Computational Biology: Issues and Applications in Oncology / [ed] Tuan Pham, Springer-Verlag New York, 2009, p. 125-147Chapter in book (Refereed)
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Abstract [en]

We present several methods based on evolutionary computation for classification of oncology data. The results in comparisons with other existing techniques show that our evolutionary computation-based methods are superior in most cases. Evolutionary computation is effective in this study because it can offer efficiency in searching in high-dimension space, particularly in nonlinear optimization and hard optimization problems. The first part of this chapter is the review of some previous work on cancer classification. The second part is an overview of evolutionary computation. The third part focuses on methods based on evolutionary computation and their applications on oncology data. Finally, this chapter concludes with some remarks and suggestions for further investigation.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2009. p. 125-147
Series
Applied Bioinformatics and Biostatistics in Cancer Research
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-125024DOI: 10.1007/978-1-4419-0811-7_6Libris ID: 11775738ISBN: 978-1-4419-0810-0 (print)ISBN: 978-1-4419-0811-7 (print)OAI: oai:DiVA.org:liu-125024DiVA: diva2:902771
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2018-01-26Bibliographically approved

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Pham, Tuan D

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  • apa
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  • nn-NB
  • sv-SE
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