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Genetic Algorithm:A Search-Based Optimization Technique


Affiliations
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, India
2 Department of Information Technology, SKN College of Engineering, Pune, India
     

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Nature has been an unlimited source of motivation to all manhood. Current activities in Soft Computing is close the progress of technologies which have source and correspondence with biological phenomenon linked with human as evolutionary computation. Soft Computing is combination of several methods as Artificial Neural Network, Fuzzy Logic and Genetic Algorithm. This paper focuses on the search based optimization technique i.e. Genetic Algorithm. Optimization is the scheme of building a something best. The biological concepts of Genetic Algorithm are discussed. Steps required for implementing Genetic Algorithm i.e. Initialization, Encoding, Genetic Operators, Mutation and Termination are described. The traveling Salesman Problem is well-known problem of search based optimization. This problem is considered for discussion. The results are discussed for different number of cities to be travelled with minimum cost function.


Keywords

Genetic Algorithm, Evolutionary Computation, Optimization, Travelling Salesman Problem.
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  • R. B. Dhumale, M. P. Ghatule, N. D. Thombare, P. M. Bangare, “An Overview of Artificial Neural Networks: Part 1”, Ciit International Journal of Artificial Intelligent Systems and Machine Learning, Feb 2018, Vol. 10, No. 3.
  • Madan Kumar Jha, Amanpreet Singh, “Application of Genetic Algorithm Technique to Inverse Modeling of Tide–Aquifer Interaction”, Environmental Earth Sciences, Vol. 71, Iss. 8, April 2013.
  • Man Lin, Chen Ding, “Parallel Genetic Algorithms for DVS Scheduling of Distributed Embedded Systems”, HPCC 2007, LNCS 4782, pp. 180–191, 2007.
  • Usama Mehboob, Junaid Qadir, Salman Ali, and Athanasios Vasilakos, “Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues”, 19 Nov 2014.
  • T. Venkat Narayana Rao, Srikanth Madiraju, “Genetic Algorithms and Programming-An Evolutionary Methodology”, International Journal of Computer Science and Information Technologies, Vol. 1 Iss. 5 , 2010,pp 427-437
  • G. Zhang et al., Multi-Level, “Optimization Models”, Decision Making, Intelligent Systems Reference Library 82.
  • Kumara Sastry, David Goldberg, Graham Kendall, “Genetic Algorithms”, Search Methodologies, pp 97-125
  • Sonam Jain, Sandeep Sahu, “The Application of Genetic Algorithm in the design of Routing Protocols in MANETs: A Survey”, International Journal of Computer Science and Information Technologies, Vol. 3, Iss. 3, 2012, pp- 4318 – 4321.
  • R. B. Dhumale, M. P. Ghatule, N. D. Thombare, P. M. Bangare, “An Overview of Artificial Neural Networks: Part 2”, Ciit International Journal of Artificial Intelligent Systems and Machine Learning, Feb 2018, Vol. 10, No. 3. (Accepted)
  • R. B. Dhumale, M. P. Ghatule, N. D. Thombare, P. M. Bangare, “An Overview of Artificial Neural Networks: Part 3”, Ciit International Journal of Artificial Intelligent Systems and Machine Learning, Feb 2018, Vol. 10, No. 3. (Accepted)
  • R. B. Dhumale, M. P. Ghatule, N. D. Thombare, P. M. Bangare, “An Overview of Artificial Neural Networks: Part 4”, Ciit International Journal of Artificial Intelligent Systems and Machine Learning, Feb 2018, Vol. 10, No. 3. (Accepted)
  • Ilona Miko, “Phenotype Variability: Penetrance and Expressivity”, penetrance and expressivity. Nature Education 1(1):137.
  • “Studying Gene Expression and Function”, Alberts B, Johnson A, Lewis J, et al. New York: Garland Science; 2002.
  • S. P. Ronald, “Genetic Algorithms and Permutation-Encoded Problems Diversity Preservation and a Study of Multimodality”. B.Eng.(Dist) R.M.I.T. School of Computer and Information Science. Faculty of Applied Science The University of South Australia
  • Nuwan I. Senaratna, “Genetic Algorithms: The Crossover-Mutation Debate”, Bachelor of Computer Science (Special) of the University of Colombo.

Abstract Views: 218

PDF Views: 3




  • Genetic Algorithm:A Search-Based Optimization Technique

Abstract Views: 218  |  PDF Views: 3

Authors

R. B. Dhumale
Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, India
N. D. Thombare
Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, India
P. M. Bangare
Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, India
M. L. Bangare
Department of Information Technology, SKN College of Engineering, Pune, India

Abstract


Nature has been an unlimited source of motivation to all manhood. Current activities in Soft Computing is close the progress of technologies which have source and correspondence with biological phenomenon linked with human as evolutionary computation. Soft Computing is combination of several methods as Artificial Neural Network, Fuzzy Logic and Genetic Algorithm. This paper focuses on the search based optimization technique i.e. Genetic Algorithm. Optimization is the scheme of building a something best. The biological concepts of Genetic Algorithm are discussed. Steps required for implementing Genetic Algorithm i.e. Initialization, Encoding, Genetic Operators, Mutation and Termination are described. The traveling Salesman Problem is well-known problem of search based optimization. This problem is considered for discussion. The results are discussed for different number of cities to be travelled with minimum cost function.


Keywords


Genetic Algorithm, Evolutionary Computation, Optimization, Travelling Salesman Problem.

References