DNA Algorithms based on Exon Shuffling

An understanding of a natural system’s information handling can lead to more effective artificial optimization techniques. There are successful optimization algorithms represented in biosystems that have proven useful in engineering applications (artificial neural networks, immune system algorithms, etc). The goal of our study is to develop a new biosystem derived an optimization algorithm which is called a DNA algorithm (DNAA) based on optimization procedures in DNA. We have focused on an analogy between optimizing procedures for protein functions using exon shuffling and those for an optimization problem in the engineering field. We used a traveling salesman problem (TSP) for evaluation of the performance of the DNAA. The DNAA could estimate approximately optimal tour routes in the 25-city TSP.