Next Article in Journal
New Methodology to Approximate Type-Reduction Based on a Continuous Root-Finding Karnik Mendel Algorithm
Previous Article in Journal
Thresholds of the Inner Steps in Multi-Step Newton Method
Article Menu

Export Article

Open AccessArticle
Algorithms 2017, 10(3), 76; doi:10.3390/a10030076

A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems

School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China
Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA
Author to whom correspondence should be addressed.
Received: 31 May 2017 / Revised: 19 June 2017 / Accepted: 19 June 2017 / Published: 30 June 2017
View Full-Text   |   Download PDF [3117 KB, uploaded 5 July 2017]   |  


As one of the evolutionary heuristics methods, genetic algorithms (GAs) have shown a promising ability to solve complex optimization problems. However, existing GAs still have difficulties in finding the global optimum and avoiding premature convergence. To further improve the search efficiency and convergence rate of evolution algorithms, inspired by the mechanism of biological DNA genetic information and evolution, we present a new genetic algorithm, called GA-TNE+DRO, which uses a novel triplet nucleotide coding scheme to encode potential solutions and a set of new genetic operators to search for globally optimal solutions. The coding scheme represents potential solutions as a sequence of triplet nucleotides and the DNA reproduction operations mimic the DNA reproduction process more vividly than existing DNA-GAs. We compared our algorithm with several existing GA and DNA-based GA algorithms using a benchmark of eight unconstrained optimization functions. Our experimental results show that the proposed algorithm can converge to solutions much closer to the global optimal solutions in a much lower number of iterations than the existing algorithms. A complexity analysis also shows that our algorithm is computationally more efficient than the existing algorithms. View Full-Text
Keywords: genetic algorithm; triplet nucleotide encoding; DNA; numerical optimization genetic algorithm; triplet nucleotide encoding; DNA; numerical optimization

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zang, W.; Zhang, W.; Zhang, W.; Liu, X. A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems. Algorithms 2017, 10, 76.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top