Solution Concept in TSP Problem Applied to Genetic Algorithm

摘要

The main purpose of this study is to propose a new representation method of chromosomes using binary matrix and new fittest criteria to be used as method for finding the optimal solution for TSP. The concept of the proposed method is taken from genetic algorithm of artificial inelegance as a basic ingredient which has been used as search algorithm to find the near-optimal solutions. Here we are introducing the new fittest criteria for crossing over, and applying the algorithm on Symmetric as well as asymmetric TSP, also presenting asymmetric problem in a new and different way. As far as the artificial inelegance is concerned, the genetic algorithm is an optimization technique based on natural evolution that is the change over a long period of time. Genetic algorithm (GAs) has been used as a search technique of many NP problems. Genetic algorithms have been successfully applied to many different types of problems, though several factors limit the success of a GA on a specific function. Problem required are good, but optimal solutions are not ideal for GAs. The manner in which points on the search space are represented is an important consideration. An acceptable performance measure or fitness value must be available. It must also be feasible to test many potential solutions.