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Evolutionary Multi-Objective Optimization for DNA Sequence Design

Evolutionary Multi-Objective Optimization for DNA Sequence Design
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Author(s): Soo-Yong Shin (Seoul National University, Korea), In-Hee Lee (Seoul National University, Korea) and Byoung-Tak Zhang (Seoul National University, Korea)
Copyright: 2008
Pages: 26
Source title: Multi-Objective Optimization in Computational Intelligence: Theory and Practice
Source Author(s)/Editor(s): Lam Thu Bui (University of New South Wales, Australia) and Sameer Alam (University of New South Wales, Australia)
DOI: 10.4018/978-1-59904-498-9.ch009

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Abstract

Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA nano-assembly, DNA microarrays and polymerase chain reaction. Sequence design involves a number of heterogeneous and conflicting design criteria. Also, it is proven as a class of NP problems. These suggest that multi-objective evolutionary algorithms (MOEAs) are actually good candidates for DNA sequence optimization. In addition, the characteristics of MOEAs including simple addition/deletion of objectives and easy incorporation of various existing tools and human knowledge into the final decision process could increase the reliability of final DNA sequence set. In this chapter, we review multi-objective evolutionary approaches to DNA sequence design. In particular, we analyze the performance of e-multi-objective evolutionary algorithms on three DNA sequence design problems and validate the results by showing superior performance to previous techniques.

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