Genetic Algorithms, Operators, and DNA Fragment Assembly

Rebecca J. Parsons, Stephanie Forrest, Christian Burks

Research output: Contribution to journalArticle

70 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)11-33
Number of pages23
JournalMachine Learning
Volume21
Issue number1
DOIs
StatePublished - Jan 1 1995
Externally publishedYes

Fingerprint

DNA sequences
Macros
Mathematical operators
DNA
Genes
Genetic algorithms
Experiments

Keywords

  • building blocks
  • DNA fragment assembly
  • edge-recombination crossover
  • genetic algorithms
  • human genome project
  • ordering problems

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Genetic Algorithms, Operators, and DNA Fragment Assembly. / Parsons, Rebecca J.; Forrest, Stephanie; Burks, Christian.

In: Machine Learning, Vol. 21, No. 1, 01.01.1995, p. 11-33.

Research output: Contribution to journalArticle

Parsons, Rebecca J. ; Forrest, Stephanie ; Burks, Christian. / Genetic Algorithms, Operators, and DNA Fragment Assembly. In: Machine Learning. 1995 ; Vol. 21, No. 1. pp. 11-33.
@article{0689e99e41664ddeb30548d20376eb50,
title = "Genetic Algorithms, Operators, and DNA Fragment Assembly",
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.",
keywords = "building blocks, DNA fragment assembly, edge-recombination crossover, genetic algorithms, human genome project, ordering problems",
author = "Parsons, {Rebecca J.} and Stephanie Forrest and Christian Burks",
year = "1995",
month = "1",
day = "1",
doi = "10.1023/A:1022613513712",
language = "English (US)",
volume = "21",
pages = "11--33",
journal = "Machine Learning",
issn = "0885-6125",
publisher = "Springer Netherlands",
number = "1",

}

TY - JOUR

T1 - Genetic Algorithms, Operators, and DNA Fragment Assembly

AU - Parsons, Rebecca J.

AU - Forrest, Stephanie

AU - Burks, Christian

PY - 1995/1/1

Y1 - 1995/1/1

N2 - 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.

AB - 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.

KW - building blocks

KW - DNA fragment assembly

KW - edge-recombination crossover

KW - genetic algorithms

KW - human genome project

KW - ordering problems

UR - http://www.scopus.com/inward/record.url?scp=0029387798&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029387798&partnerID=8YFLogxK

U2 - 10.1023/A:1022613513712

DO - 10.1023/A:1022613513712

M3 - Article

VL - 21

SP - 11

EP - 33

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

IS - 1

ER -