Integer Programming Formulation of Traveling Salesman Problems
Journal of the ACM (JACM)
Hybrid Genetic Algorithm for DNA Sequencing with Errors
Journal of Heuristics
MFCS '94 Proceedings of the 19th International Symposium on Mathematical Foundations of Computer Science 1994
Solving the Orienteering Problem Through Branch-And-Cut
INFORMS Journal on Computing
A survey of very large-scale neighborhood search techniques
Discrete Applied Mathematics
Complexity of DNA sequencing by hybridization
Theoretical Computer Science
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
DNA Sequencing--Tabu and Scatter Search Combined
INFORMS Journal on Computing
Traveling Salesman Problems with Profits
Transportation Science
DNA Sequencing by Hybridization via Genetic Search
Operations Research
Engineering Applications of Artificial Intelligence
Computers and Operations Research
Algorithm: Dealing with repetitions in sequencing by hybridization
Computational Biology and Chemistry
Matheuristics: Hybridizing Metaheuristics and Mathematical Programming
Matheuristics: Hybridizing Metaheuristics and Mathematical Programming
A math-heuristic algorithm for the DNA sequencing problem
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
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The DNA sequencing problem is aimed at reconstructing an unknown fragment of DNA (deoxyribonucleic acid) based upon a set of oligonucleotides that makes up the fragment's spectrum. Such a spectrum is the result of a hybridization experiment, in which oligonucleotides hybridize with the unknown DNA sequence. The introduction of errors (positive as well as negative) in the biological experiment phase gives rise to a challenging combinatorial optimization problem. The DNA sequencing problem can be modeled as a variation of the classical traveling salesman problem and, due to its computational complexity, it is a candidate for the design and implementation of metaheuristic algorithms. We present a hybrid algorithm, which may be seen as an approach within the recently introduced area of matheuristics, i.e., an approach in which mathematical programming techniques and metaheuristic schemes are effectively intertwined. The algorithm is tested on 400 benchmark instances from the literature and compares favorably with the best known algorithm. In addition, an explanation concerning the relation between error distribution and algorithmic performance is provided, illustrating that the way in which negative errors are distributed within the spectrum has a bearing on the overall algorithmic performance.