Evolutionary model for sequence generation

Authors:
Zhi-xiang Yin;Jin Yang;Jian-zhong Cui;Jiaxiu Zhang
Affiliations:
Department of Mathematics and Physics, Anhui University of Science and Technology, Anhui Huainan, China and Department of Control Science and Engineering, Huazhong University of Science and Techno ...;Department of Mathematics and Physics, Anhui University of Science and Technology, Anhui Huainan, China;Department of Mathematics and Physics, Anhui University of Science and Technology, Anhui Huainan, China;Department of Mathematics and Physics, Anhui University of Science and Technology, Anhui Huainan, China
Venue:
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Year:
2007

Citing 8
Cited 0

Molecular computation of solutions to combinatorial problems

Science
DNASequencesGenerator: A Program for the Construction of DNA Sequences

DNA 7 Revised Papers from the 7th International Workshop on DNA-Based Computers: DNA Computing
Stochastic Local Search Algorithms for DNA Word Design

DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
Algorithms for Testing That Sets of DNA Words Concatenate without Secondary Structure

DNA8 Revised Papers from the 8th International Workshop on DNA Based Computers: DNA Computing
Codeword design and information encoding in DNA ensembles

Natural Computing: an international journal
Hairpin-based state machine and conformational addressing: Design and experiment

Natural Computing: an international journal
Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing

IEEE Transactions on Evolutionary Computation
Computational Intelligence for Optimization

Computational Intelligence for Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

DNA computing is being applied to solve problems in combinatorial optimization, logic and Boolean circuits. Breakthrough solutions in combinatorial optimization are the most impressive area of success but, in order to solve combinatorial optimization problems, problems related to the reliability of biological operators, stable DNA expressions, processing speed, expandability and the universality of evaluation criteria must be solved. This study implements a DNA sequence generation system that minimizes errors using DNA coding based on evolutionary models and performs simulation using biological experiment operators. The usefulness of this system is evaluated by applying the Hamiltonian Path Problem (HPP) in the form of a genetic algorithm. The proposed system generates sequences with minimal errors, as compared to existing systems, and identifies optimal solutions for combinatorial optimization problems in significantly reduced processing times.