International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:09 No:10 55 96110-1414 IJBAS-IJENS © December 2009 IJENS I J E N S Solving ISP Problem by Using Genetic Algorithm Fozia Hanif Khan 1 , Nasiruddin Khan 2 , Syed Inayatulla 3 , And Shaikh Tajuddin Nizami 4 1 Fozia Hanif Khan, Fsazaia Degree college, Faisal, Phone:03333301050, email: mf_khans@hotmail.com. 1 Prof. Dr Nasiruddin Khan, University of Karachi, Ph:o3333101945, email: drprof_khan@yahoo.com 1 Syad Inayatullah University of Karachi, Ph: 032221159022, email:inayt_ku@yahoo.com 1 Shaikh Tajuddin Nizami, NED University, Ph: 03002761859 email: Abstract -- 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. Index Term-- Genetic algorithm, fittest criteria, asymmetric travelling salesman problem. I. INTRODUCTION 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. In nature the fittest individual is most likely to survive and mutate, therefore the next generation should be fitter and healthier because they were bred from healthy parents. The same idea can be applied on the TSP problem by first finding the different solutions and then combine those, which are the fittest solutions among them, in order to create a new and healthy solution and should be optimal or near optimal according to the problem. TSP is a well known problem for finding the optimal path which can be solved by various methods. Many algorithms have been developed for TSP but here we are using the concept of genetic algorithm. Other approximation techniques for finding near optimum solutions for TSP based on heuristics are proposed in the literature such as [1] simulated annealing [2], ant colonies [3], genetic algorithms (GA) [4] and [5]. John Holland’s pioneering book “Adaptation in natural Artificial System (1975, 1992) showed how the evolutionary process can be applied to solve a wide variety of problems using a highly parallel technique that is now called genetic algorithm. Genetic Algorithms have been applied to a large number of real world problems. One of the first such applications was a gas pipeline control system created by [6], [7] mentions several GA applications including message routing, scheduling, and automated design. Entire conferences have been devoted to applications of Genetic Algorithms and evolutionary techniques to specific disciplines, such as Image Analysis, Signal Processing and Telecommunications [8]. The proposed genetic algorithm in this paper build on much work done by previous researchers [4], but we introduces additional improvements, providing an algorithm for symmetric as well as Asymmetric TSP, here we are implementing the new fittest criteria as well as new representation of asymmetric matrix and improving our solution by applying the crossover and mutation again and again in order to get the optimal solution. G ENETIC ALGORITHMS The Genetic Algorithm involves the following basic steps.  Evaluation  Cross over  Mutation Here we are trying to describe the main function used by algorithm to find the shortest closed path through a group of two dimensional positions.