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Fuzzy inspired hybrid genetic approach to optimize travelling salesman problem

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In our work we have defined a genetic approach by combining fuzzy approach along with genetics. In this work we have implemented the modified DPX crossover to improve genetic approach. The work is implemented in MATLAB environment and obtained results shows the define approach has optimized the existing genetic algorithm results. | ISSN:2249-5789 Bindu et al , International Journal of Computer Science & Communication Networks,Vol 2(3), 416-420 Fuzzy Inspired Hybrid Genetic Approach to Optimize Travelling Salesman Problem Bindu Student, JMIT Radaur binduaahuja@gmail.com Mrs. Pinki Tanwar Asstt. Prof, CSE, JMIT Radaur pinki.tanwar@gmail.com Abstract One of the category of algorithm Problems are basically exponential problems. These problems are basically exponential problems and take time to find the solution. In the present work we are optimising one of the common NP complete problem called Travelling Salesman Problem. In our work we have defined a genetic approach by combining fuzzy approach along with genetics. In this work we have implemented the modified DPX crossover to improve genetic approach. The work is implemented in MATLAB environment and obtained results shows the define approach has optimized the existing genetic algorithm results. Keywords: Genetics, Travelling Salesman Problem, NP complete, Fuzzy approach, DPX crossover 1. Introduction Travelling salesman problem is the most common used algorithmic concept used by most of the researchers working on optimizing the network communication. The Travelling salesman problem is easy to define but very hard to solve it. The problem is to find the shortest possible tour through a set of N vertices so that each vertex can visit exactly once. This problem is known to be NP-hard, and cannot be solved exactly in polynomial time. To solve this problem in the effective time is always a challenge for the researchers. We are also working in the same direction to find the optimal solution to the problem. The problem can have number of feasible solutions but the outcome that will gives the best result in terms of time and space will be represented as the optimal solution. This means that a very large number of solution need to be tested in order to determine which solution is optimal[1]. In general terms or discussions in reference to .