Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the travel...

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Published in:Computational Intelligence and Neuroscience 2017-01, Vol.2017 (2017), p.1-7
Main Authors: Mohamd Shoukry, Alaa, Hussain, Ijaz, Nauman Sajid, M., Shad, Muhammad Yousaf, Hussain, Abid, Gani, Showkat
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Chromosomes
Computer applications
Computer science
Cooperative learning
Crossovers
Cybernetics
Evolutionary algorithms
Genetic algorithms
Heuristic
International conferences
Mutation
Neural networks
Operations research
Population
Traveling salesman problem
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cited_by cdi_FETCH-LOGICAL-a568t-ebaa494d47291eef8c7f1447e2f5df6c7f8c5951a825c980116cc6496da23f493
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container_title Computational Intelligence and Neuroscience
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creator Mohamd Shoukry, Alaa
Hussain, Ijaz
Nauman Sajid, M.
Shad, Muhammad Yousaf
Hussain, Abid
Gani, Showkat
description Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.
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subjects Algorithms
Approximation
Chromosomes
Computer applications
Computer science
Cooperative learning
Crossovers
Cybernetics
Evolutionary algorithms
Genetic algorithms
Heuristic
International conferences
Mutation
Neural networks
Operations research
Population
Traveling salesman problem
title Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator
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