An Enhanced Genetic Algorithm for DNA Sequencing by Hybridization with Positive and Negative Errors

Abstract

Abstract. This paper describes a genetic algorithm for the DNA sequencing problem. The algorithm allows the input spectrum to contain both positive and negative errors as could be expected from a hybridization experiment. The main features of the algorithm include a preprocessing step that reduces the size of the input spectrum and an efficient local optimization. In experimental tests, the algorithm performed very well against existing algorithms. The algorithm also performed very well on a large data set generated in this paper from real genomes data.

Sequencing by hybridization: an enhanced crossover operator for a hybrid genetic algorithm

“…Indeed, even in the presence of repetitions, this algorithm has exceptionally good performance. In Bui and Youseef (2004), a genetic algorithm is proposed to deal with both type of errors, and compared with the one in Blazewicz et al (2002b). In a more recent work (Blazewicz et al 2004), the Tabu search algorithm (Blazewicz et al 2000) is enhanced by scatter search.…”

Unified encoding for hyper-heuristics with application to bioinformatics

“…This can be done by combinatorial algorithms. There have been many successful solutions already presented in the literature (Lysov et al 1988;Dramanac et al 1989;Pevzner 1989;Bui and Youssef 2004;Blazewicz and Kasprzak 2003;Blazewicz et al 1997Blazewicz et al , 2002Blazewicz et al , 2004Blazewicz et al , 2006a. This paper presents a unique insight into the DNA sequencing by hybridization problem from the perspective of developing generic search techniques which could be applied to different combinatorial optimization problems.…”