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Combination of Multiple Neural Networks to Solve Travelling Salesman Problem using Genetic Algorithm

by Anmol Aggarwal, Jasdeep Singh Bhalla
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 11
Year of Publication: 2013
Authors: Anmol Aggarwal, Jasdeep Singh Bhalla
10.5120/14053-1780

Anmol Aggarwal, Jasdeep Singh Bhalla . Combination of Multiple Neural Networks to Solve Travelling Salesman Problem using Genetic Algorithm. International Journal of Computer Applications. 81, 11 ( November 2013), 1-6. DOI=10.5120/14053-1780

@article{ 10.5120/14053-1780,
author = { Anmol Aggarwal, Jasdeep Singh Bhalla },
title = { Combination of Multiple Neural Networks to Solve Travelling Salesman Problem using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 11 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number11/14053-1780/ },
doi = { 10.5120/14053-1780 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:45.871324+05:30
%A Anmol Aggarwal
%A Jasdeep Singh Bhalla
%T Combination of Multiple Neural Networks to Solve Travelling Salesman Problem using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 11
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the already recognized NP Complete problem which is Travelling Salesman Problem which is also known as TSP, is addressed and its performance is analyzed. For a large and complex data set, a particular neural network is needed to focus on a particular component of the problem, after which combining of all the networks together is required. The basic ideology behind this multiple neural network arrangement is that, n-independently different neural networks are trained separately and then their respective outputs (generated independently) are combined together to form the resultant classifier. A new combining technique (for combining outputs of multiple neural networks into one) has been proposed and utilized for evidence blend in the simulation part of the method. Its performance is analyzed on the bases of simulation and it shows some notably enhanced outcomes.

References
  1. Akshay Gupta, "Synthesis and Performance Analysis of Recurrent Fuzzy Multilayer Perceptron for Speech Recognition", an IEEE International Conference on the Methods & Models in Computer Science, held in December 2010
  2. Akshay Gupta, Khushboo Aggarwal, "Multiple Neural Network Architecture for the Travelling Salesman Problem", International Transactions on Applied Sciences & Technology (ITAST) Volume :1 No 1 May,year 2011.
  3. Al-Dulaimi, Hamza A. Ali and Buthainah Fahran "Enhanced Traveling Salesman Problem Solvingby Genetic Algorithm Technique (TSPGA)", The World Academy of Science,. . Engineering and Technology 14 2008.
  4. Hashem, S. , B,. Schmeiser , "Approximating a function and its derivatives using MSE-optimal linear combinations of trained feedforward neural networks", The Proceedings of the Joint Conference on Neural Networks pg. 617–620
  5. David W. Opitz, Jude W. Shavlik, "A Genetic Algorithm Approach for Creating Neural-Network Ensembles", Combining ANNs, A. Sharkey, Springer-Verlag, London, pp. 79-97, 1999.
  6. Jin H. Kim Sun Cho, , "Combining Multiple Neural Networks by Fuzzy Integral for Robust Classification" published in IEEE Transactions(System, Man and Cybernetics), Volume 25, No. 2, February 1995.
  7. Krishnan Chander, "Reservoir characterization using well log data with the aid of soft computing tools", though it is an unpublished article.
  8. Durbin R. Rumelhart D. E. ,Chauvin Y. , "Backpropagation: The basic theory, architecture and application" Pgs 1 to 34. Lawrence Erlbaum, Hillsdale, NJ.
  9. Takayama K, Morva A, Obata Y, Nagai T, Fujikawa M, Hattori Y,. , "Formula optimization of theophylline controlled-release tablet based on artificial neural networks", Release; 68:175-186
  10. Simon Haykin, "Neural Networks, A Comprehensive Foundation", Second Englewood Cliffs, NJ Prentice-Hall, 1999:156-254.
Index Terms

Computer Science
Information Sciences

Keywords

Travelling Salesman Problem Artificial Intelligence Multiple Neural Networks Genetic Algorithm ANNs neural networks.