Evolutionary DNA Computing Algorithm for Job Scheduling Problem

DNA computing techniques have interesting properties such as vast parallel computation attainment, organic edges, and tiny parts. These properties have attracted researchers from various fields (Bioinformatics, Biochemistry, and others). These techniques mainly rest on biochemical responses of molecules of DNA. Nonetheless, these biochemical responses might anneal in unsystematic fashion and conceivably generate inappropriate computations. This motivates prospects to utilize evolutionary computation as it lays importance on probabilistic and optimization search approaches. In this research study, the ability of DNA computing is demonstrated and verified by selecting the job scheduling problem (JSP). JSP can be easily tackled by a human or by using standard computers. A proposed evolutionary DNA algorithm is presented in this paper to solve the JSP; the proposed technique produces promising and better results than the standard DNA computing algorithm. Through adding supportive operations to the evolutionary operations, the performance becomes better; in addition, it has more solutions at the end, and therefore, the possibility of having an optimum or near optimum solution is increased, and the average number of solutions is improved.