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
Tabu Search
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
Sequencing by hybridization with errors: handling longer sequences
Theoretical Computer Science
DNA Sequencing by Hybridization via Genetic Search
Operations Research
Engineering Applications of Artificial Intelligence
An ant colony optimization algorithm for DNA sequencing by hybridization
Computers and Operations Research
Algorithm: Dealing with repetitions in sequencing by hybridization
Computational Biology and Chemistry
Applications of artificial intelligence in bioinformatics: A review
Expert Systems with Applications: An International Journal
A math-heuristic algorithm for the DNA sequencing problem
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
New constructive heuristics for DNA sequencing by hybridization
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Multi-level ant colony optimization for DNA sequencing by hybridization
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
A hybrid algorithm for the DNA sequencing problem
Discrete Applied Mathematics
Tabu search algorithm for DNA sequencing by hybridization with multiplicity information available
Computers and Operations Research
Hi-index | 0.00 |
In the paper, a new hybrid genetic algorithm solving the DNA sequencing problem with negative and positive errors is presented. The algorithm has as its input a set of oligonucleotides coming from a hybridization experiment. The aim is to reconstruct an original DNA sequence of a known length on the basis of this set. No additional information about the oligonucleotides nor about the errors is assumed. Despite that, the algorithm returns for computationally hard instances surprisingly good results, of a very high similarity to original sequences.