Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Hybrid Genetic Algorithm for DNA Sequencing with Errors
Journal of Heuristics
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Complexity of DNA sequencing by hybridization
Theoretical Computer Science
DNA Sequencing--Tabu and Scatter Search Combined
INFORMS Journal on Computing
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
Non-identical parallel machine scheduling using genetic algorithm
Expert Systems with Applications: An International Journal
Accurate Reconstruction for DNA Sequencing by Hybridization Based on a Constructive Heuristic
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Tabu search algorithm for DNA sequencing by hybridization with multiplicity information available
Computers and Operations Research
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This paper presents a genetic algorithm for an important computational biology problem. The problem appears in the computational part of a new proposal for DNA sequencing denominated sequencing by hybridization. The general usage of this method for real sequencing purposes depends mainly on the development of good algorithmic procedures for solving its computational phase. The proposed genetic algorithm is a modified version of a previously proposed hybrid genetic algorithm for the same problem. It is compared with two well suited meta-heuristic approaches reported in the literature: the hybrid genetic algorithm, which is the origin of our proposed variant, and a tabu-scatter search algorithm. Experimental results carried out on real DNA data show the advantages of using the proposed algorithm. Furthermore, statistical tests confirm the superiority of the proposed variant over the state-of-the-art heuristics.