Impact of Crossover Probability on Symmetric Travel Salesman Problem Efficiency

Dublin Core

Title

Impact of Crossover Probability on Symmetric Travel Salesman Problem Efficiency

Subject

Genetic Algorithm
Crossover
mutation
TSP

Description

Genetic algorithm (GA) is a powerful evolutionary searching technique that is used successfully to solve and optimize problems in different research areas. Genetic Algorithm (GA) considered as one of optimization methods used to solve Travel salesman Problem (TSP). The feasibility of GA in finding TSP solution is dependent on GA operators; encoding method, population size, number of generations in general. In specific, crossover and its probability play a significant role in finding possible solution for Symmetric TSP (STSP). In addition, crossover should be determined and enhanced in term reaching optimal or at least near optimal. In This paper, we spot the light on using modified crossover method called Modified sequential constructive crossover and its impact on reaching optimal solution. To justify the relevance of parameters value in solving TSP, a set comparative analysis conducted on different crossover methods values.

Creator

Alsharafat, Wafa' Slaibi
Abu-owida, Suhila Farhan

Source

International Journal of Interactive Mobile Technologies (iJIM); Vol. 9 No. 1 (2015); pp. 60-63
1865-7923

Publisher

International Association of Online Engineering (IAOE), Vienna, Austria

Date

2015-01-24

Rights

Copyright (c) 2017 Wafa' Slaibi Alsharafat, Suhila Farhan Abu-owida

Relation

Format

application/pdf

Language

eng

Type

info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Non-refereed Article

Identifier

Citation

Wafa Alsharafat' Slaibi and Abu-owida, Suhila Farhan, Impact of Crossover Probability on Symmetric Travel Salesman Problem Efficiency, International Association of Online Engineering (IAOE), Vienna, Austria, 2015, accessed November 6, 2024, https://igi.indrastra.com/items/show/1109

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