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A population-based ant colony optimization approach for DNA sequence optimization

Kurniawan, Tri Basuki and Ibrahim, Zuwairie and Khalid, Noor Khafifah and Khalid, Marzuki (2009) A population-based ant colony optimization approach for DNA sequence optimization. In: 3rd Asia International Conference on Modelling and Simulation. IEEE, Washington, DC, USA, pp. 246-251. ISBN 978-0-7695-3648-4

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Official URL: http://dx.doi.org/10.1109/AMS.2009.79


DNA computing is a new computing paradigm which uses bio-molecular as information storage media and biochemical tools as information processing operators. It has shows many successful and promising results for various applications. Since DNA reactions are probabilistic reactions, it can cause the different results for the same situations, which can be regarded as errors in the computation. To overcome the drawbacks, much works have focused to design the error-minimized DNA sequences to improve the reliability of DNA computing. In this research, Population-based Ant Colony Optimization (P-ACO) is proposed to solve the DNA sequence optimization. PACO approach is a meta-heuristic algorithm that uses some ants to obtain the solutions based on the pheromone in their colony. The DNA sequence design problem is modelled by four nodes, representing four DNA bases (A, T, C, and G). The results from the proposed algorithm are compared with other sequence design methods, which are Genetic Algorithm (GA), and Multi-Objective Evolutionary Algorithm (MOEA) methods. The DNA sequences optimized by the proposed approach have better result in some objective functions than the other methods.

Item Type:Book Section
Uncontrolled Keywords:ant colony, DNA sequence optimization, population-based ant colony optimization (P-ACO)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
Divisions:Electrical Engineering
ID Code:11870
Deposited By: Nor Asmida Abdullah
Deposited On:21 Jan 2011 10:23
Last Modified:02 Oct 2017 04:57

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