Genetic Algorithm Performance with Different Selection Strategies in Solving TSP

A genetic algorithm (GA) has several genetic operators that can be modified to improve the performance of particular implementations. These operators include parent selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection...

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Main Authors: Noraini, Mohd Razali, Geraghty, John
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/2609/1/WCE_noraini.pdf
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author Noraini, Mohd Razali
Geraghty, John
author_facet Noraini, Mohd Razali
Geraghty, John
author_sort Noraini, Mohd Razali
collection UMP
description A genetic algorithm (GA) has several genetic operators that can be modified to improve the performance of particular implementations. These operators include parent selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection. This paper presents the comparison of GA performance in solving travelling salesman problem (TSP) using different parent selection strategy. Several TSP instances were tested and the results show that tournament selection strategy outperformed proportional roulette wheel and rank-based roulette wheel selections, achieving best solution quality with low computing times. Results also reveal that tournament and proportional roulette wheel can be superior to the rank-based roulette wheel selection for smaller problems only and become susceptible to premature convergence as problem size increases.
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spelling UMPir26092018-02-08T03:53:56Z http://umpir.ump.edu.my/id/eprint/2609/ Genetic Algorithm Performance with Different Selection Strategies in Solving TSP Noraini, Mohd Razali Geraghty, John TS Manufactures A genetic algorithm (GA) has several genetic operators that can be modified to improve the performance of particular implementations. These operators include parent selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection. This paper presents the comparison of GA performance in solving travelling salesman problem (TSP) using different parent selection strategy. Several TSP instances were tested and the results show that tournament selection strategy outperformed proportional roulette wheel and rank-based roulette wheel selections, achieving best solution quality with low computing times. Results also reveal that tournament and proportional roulette wheel can be superior to the rank-based roulette wheel selection for smaller problems only and become susceptible to premature convergence as problem size increases. 2011 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2609/1/WCE_noraini.pdf Noraini, Mohd Razali and Geraghty, John (2011) Genetic Algorithm Performance with Different Selection Strategies in Solving TSP. In: The 2011 International Conference of Computational Intelligence and Intelligent Systems , 6-8 July, 2011 , Imperial College, London. .
spellingShingle TS Manufactures
Noraini, Mohd Razali
Geraghty, John
Genetic Algorithm Performance with Different Selection Strategies in Solving TSP
title Genetic Algorithm Performance with Different Selection Strategies in Solving TSP
title_full Genetic Algorithm Performance with Different Selection Strategies in Solving TSP
title_fullStr Genetic Algorithm Performance with Different Selection Strategies in Solving TSP
title_full_unstemmed Genetic Algorithm Performance with Different Selection Strategies in Solving TSP
title_short Genetic Algorithm Performance with Different Selection Strategies in Solving TSP
title_sort genetic algorithm performance with different selection strategies in solving tsp
topic TS Manufactures
url http://umpir.ump.edu.my/id/eprint/2609/1/WCE_noraini.pdf
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AT geraghtyjohn geneticalgorithmperformancewithdifferentselectionstrategiesinsolvingtsp