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 25, 2024, https://igi.indrastra.com/items/show/1109