This paper aims to highlight the motivations for investigating genetic algorithms to solve the DNA Fragments Assembly problem (DNA_FA). DNA_FA is an optimization problem that attempts to reconstruct the original DNA sequence by finding the shortest DNA sequence from a given set of fragments. We showed that the DNA_FA optimization problem is a special case of the two well-known optimization problems: The Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP). TSP and QAP are important problems in the field of combinatorial optimization and for which there exists an abundant literature. Genetic Algorithms (GA) applied to these problems have led to very satisfactory results in practice. In the perspective of designing efficient genetic algorithms to solve DNA_FA we showed the existence of a polynomial-time reduction of DNA-FA into TSP and QAP enabling us to point out some technical similarities in terms of solutions and search space complexity. We then conceptually designed a genetic algorithm platform for solving the DNA-FA problem inspired from the existing efficient genetic algorithms in the literature solving TSP and QAP problems. This platform offers several ingredients enabling us to create several variants of GA solvers for the DNA assembly optimization problems.

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 polynomialtime 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.