Genetic Algorithms, Operators, and DNA Fragment Assembly

Authors:
Rebecca J. Parsons;Stephanie Forrest;Christian Burks
Affiliations:
Los Alamos National Laboratory. Current Address: University of Central Florida, Department of Computer Science, Orlando, FL 32816-0362. rebecca@cs.ucf.edu;University of New Mexico, Department of Computer Science, Albuquerque, NM 87131-1386. forrest@cs.unm.edu;Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, MS K710, Los Alamos, NM 87545. cb@t10.lanl.gov
Venue:
Machine Learning - Special issue on applications in molecular biology
Year:
1995

Citing 0
Cited 13

Efficiency speed-up strategies for evolutionary computation: fundamentals and fast-GAs

Applied Mathematics and Computation
Exact and Heuristic Algorithms for the DNA Fragment Assembly Problem

CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Bioinformatics—an introduction for computer scientists

ACM Computing Surveys (CSUR)
Multiniche crowding in genetic algorithms and its application to the assembly of dna restriction-fragments

Evolutionary Computation
DNA fragment assembly using a grid-based genetic algorithm

Computers and Operations Research
Seeding strategies and recombination operators for solving the DNA fragment assembly problem

Information Processing Letters
A new local search algorithm for the DNA fragment assembly problem

EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Elementary landscape decomposition of the quadratic assignment problem

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
DNA fragment assembly: an ant colony system approach

EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
DNA fragment assembly by ant colony and nearest neighbour heuristics

ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
An efficient genome fragment assembling using GA with neighborhood aware fitness function

Applied Computational Intelligence and Soft Computing - Special issue on Awareness Science and Engineering
Benchmark datasets for the DNA fragment assembly problem

International Journal of Bio-Inspired Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We study different genetic algorithm operators for one permutation problem associated with the Human Genome Project—the assembly of DNA sequence fragments from a parent clone whose sequence is unknown into a consensus sequence corresponding to the parent sequence. The sorted-order representation, which does not require specialized operators, is compared with a more traditional permutation representation, which does require specialized operators. The two representations and their associated operators are compared on problems ranging from 2K to 34K base pairs (KB). Edge-recombination crossover used in conjunction with several specialized operators is found to perform best in these experiments; these operators solved a 10KB sequence, consisting of 177 fragments, with no manual intervention. Natural building blocks in the problem are exploited at progressively higher levels through “macro-operators.” This significantly improves performance.