Paper Info
Title
Multi-objective evolutionary algorithm for DNA codeword design
Abstract
ABSTRACTFinding effective ways to encode data into DNA is not an easy task since the search space grows exponentially respect to the length of the oligonucleotides (single DNA strands) used in the task. In short, the problem of DNA Codeword Design (CD) is the optimization problem of finding large sets of short oligonucleotides which satisfy certain non-crosshybridizing constraints (combinatorial and/or thermodynamic). In this paper, a Multi-objective Evolutionary Algorithm (MoEA-CD) that exploits the structural properties of the DNA space to improve the speed and quality of candidate solutions to the CD problem is proposed. To this purpose, we consider the CD problem as a set covering problem, and we introduce two approximations mechanisms for computing the coverage and overlap of sets in such DNA space. The algorithm is tested in different DNA space dimensions, and our results indicate that MoEA-CD using such approximation mechanism maintains an excellent performance as the dimension of the search space is increased.
Year
DOI
Venue
2019
10.1145/3321707.3321855
Genetic and Evolutionary Computation Conference
Keywords
Field
DocType
Multi-objective optimization, Evolutionary Algorithm, DNA Codeword Design, non-cross-hybridization (nxh), the h-distance, Set covering problem, DNA encoding
Evolutionary algorithm,Computer science,Theoretical computer science,Code word,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jeisson Prieto100.34
Jonatan Gómez224129.70
Elizabeth León3394.82