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DNA Sequence Design using $\varepsilon$ -Multiobjective Evolutionary Algorithm  

Shin Soo-Yong (서울대학교 컴퓨터공학부)
Lee In-Hee (서울대학교 컴퓨터공학부)
Zhang Byoung-Tak (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.12, 2005 , pp. 1217-1228 More about this Journal
Abstract
Recently, since DNA computing has been widely studied for various applications, DNA sequence design which is the most basic and important step for DNA computing has been highlighted. In previous works, DNA sequence design has been formulated as a multi-objective optimization task, and solved by elitist non-dominated sorting genetic algorithm (NSGA-II). However, NSGA-II needed lots of computational time. Therefore, we use an $\varepsilon$- multiobjective evolutionarv algorithm ($\varepsilon$-MOEA) to overcome the drawbacks of NSGA-II in this paper. To compare the performance of two algorithms in detail, we apply both algorithms to the DTLZ2 benchmark function. $\varepsilon$-MOEA outperformed NSGA-II in both convergence and diversity, $70\%$ and $73\%$ respectively. Especially, $\varepsilon$-MOEA finds optimal solutions using small computational time. Based on these results, we redesign the DNA sequences generated by the previous DNA sequence design tools and the DNA sequences for the 7-travelling salesman problem (TSP). The experimental results show that $\varepsilon$-MOEA outperforms the most cases. Especially, for 7-TSP, $\varepsilon$-MOEA achieves the comparative results two tines faster while finding $22\%$ improved diversity and $92\%$ improved convergence in final solutions using the same time.
Keywords
DNA sequence design; NSGA-II; MOEA;
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