Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Abstract: This paper aims to highlight the motivations for investigating genetic algorithms (GAs) to solve the DNA Fragment Assembly (DNAFA) problem. DNAFA problem is an optimization problem that attempts to reconstruct an original DNA sequence by finding the shortest DNA sequence from a given set of fragments. This paper is a continuation of our previous research paper in which the existence of a polynomial-time reduction of DNAFA into the Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP) was discussed. Taking advantage of this reduction, this work conceptually designed a genetic algorithm (GA) platform to solve the DNAFA problem. This platform offers several ingredients enabling us to create several variants of GA solvers for the DNAFA optimization problems. The main contribution of this paper is the designing of an efficient GA variant by carefully integrating different GAs operators of the platform. For that, this work individually studied the effects of different GAs operators on the performance of solving the DNAFA problem. This study has the advantage of benefiting from prior knowledge of the performance of these operators in the contexts of the TSP and QAP problems. The best designed GA variant shows a significant improvement in accuracy (overlap score) reaching more than 172% of what is reported in the literature.

Hachemi Bennaceur, Meznah Almutairy and Nora Alqhtani. “Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.3 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140333

@article{Bennaceur2023,
title = {Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140333},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140333},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Hachemi Bennaceur and Meznah Almutairy and Nora Alqhtani}
}