Book Details

SOLVING TRAVELLING SALESMAN PROBLEM EFFICIENTLY USING GENETIC ALGORITHM

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).

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Abstract

The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the concepts of natural genetics and natural selection theories. The main idea 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 idea 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 is proposed. Now 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.

References

[1] Bineet Mishra, Rakesh Kumar Patnaik “Genetic Algorithm and Its Variants: Theory And Applications”, Department of Electronics and Communication Engineering. NIT ROURKELA.

[2] Phillip David Power, “Non Linear Multi Layer Perceptron Channel Equalisation”,Chapter 4 „Genetic Algorithm Optimisation?, in IEEE Transactions,The Queen University of Belfast,April.

[3] Tzung-Pei Hong,”Evolution of Appropriate Crossover andMutation Operators in a Genetic Process” Department of Information Management IShou University,Kaohsiung, 84008, Taiwan, R.O.C.

[4] Tzung-Pei Hong,”Evolution of Appropriate Crossover andMutation Operators in a Genetic Process” Department of Information Management IShou University,Kaohsiung, 84008, Taiwan, R.O.C.

[5] T. B&a&ck, “Optimal mutation rates in genetic search,” in Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 2-8, 1993.

[6] B. Freisleben and P. Merz, “A Genetic local search Algorithm for Solving Symmetric and Asymmetric Travelling Salesman Problems,” International Conference On Evolutionary Computation, pp. 616- 621, 1996.

[7] Seniw, D., A Genetic algorithm for the Travelling Salesman Problem, MSc Thesis, University of North Carolina, at Charlotte. http://www.heatonresearch.com/articales/65/page1.ht ml. 1996.

[8] B. Freisleben and P. Merz, “A Genetic local search Algorithm for Solving Symmetric and Asymmetric Travelling Salesman Problems,” International Conference On Evolutionary Computation, pp. 616- 621,1996

Keywords

Genetic Algorithm, Chromosomes, Fittest criteria.

Image
  • Format Volume 1, Issue 2, No 3, 2013.
  • Copyright All Rights Reserved ©2013
  • Year of Publication 2013
  • Author Dr.S.S.Dhenakaran, S.Sangari Devi
  • Reference IJCS-020
  • Page No 105-110

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