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Impact of Double Operators on the Performance of a Genetic Algorithm for Solving the Traveling Salesman Problem

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7076))

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Abstract

Genetic algorithms are a frequently used method for search and optimization problem solving. They have been applied very successfully to many NP-hard problems, among which the traveling salesman problem, which is also considered in this paper, is one of the most famous representative ones. A genetic algorithm usually makes use only of single mutation and a single crossover operator. However, three modes for determination which of the double crossover and mutation operators should be used in a given moment are presented. It has also been tested if there is a positive impact on the performance if double genetic operators are used. Experimental analysis conducted on several instances of the symmetric traveling salesman problem showed that it is possible to achieve better results by adaptively adjusting the usage of double operators, rather than by combining any single genetic operators.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2b, 31000, Osijek, Croatia

    Goran Martinovic & Drazen Bajer

Authors
  1. Goran Martinovic
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  2. Drazen Bajer
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Department of Electronics and Telecommunications, Jadavpur University, 700032, Kolkata, India

    Swagatam Das

  4. ANITS, Visakhapatnam, India

    Suresh Chandra Satapathy

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© 2011 Springer-Verlag Berlin Heidelberg

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Martinovic, G., Bajer, D. (2011). Impact of Double Operators on the Performance of a Genetic Algorithm for Solving the Traveling Salesman Problem. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_36

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  • DOI: https://doi.org/10.1007/978-3-642-27172-4_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27171-7

  • Online ISBN: 978-3-642-27172-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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