Classifier systems and genetic algorithms
Artificial Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Coding and information theory
Distinguishing string selection problems
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Efficient approximation algorithms for the Hamming center problem
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Ant algorithms for discrete optimization
Artificial Life
On the closest string and substring problems
Journal of the ACM (JACM)
Approximation Algorithms for NP-Hard Problems
ACM SIGACT News
Genetic Algorithms and Machine Learning
Machine Learning
Optimal Solutions for the Closest-String Problem via Integer Programming
INFORMS Journal on Computing
A parallel multistart algorithm for the closest string problem
Computers and Operations Research
Exact Solutions for Closest String and Related Problems
ISAAC '01 Proceedings of the 12th International Symposium on Algorithms and Computation
More efficient algorithms for closest string and substring problems
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Parallel genetic algorithm and parallel simulated annealing algorithm for the closest string problem
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
A GRASP algorithm for the Closest String Problem using a probability-based heuristic
Computers and Operations Research
A heuristic algorithm based on Lagrangian relaxation for the closest string problem
Computers and Operations Research
An efficient two-phase ant colony optimization algorithm for the closest string problem
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Algorithms for sequence analysis are of central importance in computational molecular biology and coding theory. A very interesting problem in this field is the Closest String Problem (CSP) which consists in finding a string t with minimum Hamming distance from all strings in a given finite set. To overcome the NP-hardness of the CSP problem, we propose a new algorithm, called Ant-CSP, based on the Ant Colony Optimization metaheuristic. To assess its effectiveness and robustness, we compared it with two state-of-the-art algorithms for the CSP problem, respectively based on the simulated annealing and the genetic algorithm approaches. Experimental results show that Ant-CSP outperforms both of them in terms of quality of solutions and convergence speed.