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Genetic Algorithm Performance with Different Selection Strategies in Solving TSP

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. .

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Genetic algorithm; Selection; Travelling salesman problem; Optimization
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Mechanical Engineering
Depositing User: Dr Noraini Mohd Razali
Date Deposited: 21 Jun 2012 07:12
Last Modified: 08 Feb 2018 03:53
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