Genetic Algorithms, Operators, and DNA Fragment Assembly
Machine Learning - Special issue on applications in molecular biology
Trie-Based Data Structures for Sequence Assembly
CPM '97 Proceedings of the 8th Annual Symposium on Combinatorial Pattern Matching
Exact and Heuristic Algorithms for the DNA Fragment Assembly Problem
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Seeding strategies and recombination operators for solving the DNA fragment assembly problem
Information Processing Letters
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Iterated local search for de novo genomic sequencing
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling
Applied Soft Computing
Bee algorithms for solving DNA fragment assembly problem with noisy and noiseless data
Proceedings of the 14th annual conference on Genetic and evolutionary computation
International Journal of Grid and Utility Computing
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
Benchmark datasets for the DNA fragment assembly problem
International Journal of Bio-Inspired Computation
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In this paper we propose and study the behavior of a new heuristic algorithm for the DNA fragment assembly problem: PALS. The DNA fragment assembly is a problem to be solved in the early phases of the genome project and thus is very important since the other steps depend on its accuracy. This is an NP-hard combinatorial optimization problem which is growing in importance and complexity as more research centers become involved on sequencing new genomes. Various heuristics, including genetic algorithms, have been designed for solving the fragment assembly problem, but since this problem is a crucial part of any sequencing project, better assemblers are needed. Our proposal is a very efficient assembler that allows to find optimal solutions for large instances of this problem, considerably faster than its competitors and with high accuracy.