Genetic Algorithm Adopting Immigration Operator to Solve the Asymmetric Traveling Salesman Problem

In this work, we are interested in improving the performance of genetic algorithm (GA) to solve the Asymmetric Traveling Salesman Problem (ATSP). Several approaches have been developed with genetic algorithms based on the adaptation and improvement of different standard genetic operators. We proposes a new GA adopting immigration strategies to maitain diversity and to perform more the genetic algorithm. Experimental results on series of standard instances of ATSP show that the proposed structured memory immigration scheme in GA effectively improves the performance of GAs.