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Trucking simulation using genetic algorithms


Trucking simulation using genetic algorithms

Deng, Qixia (2003) Trucking simulation using genetic algorithms. Masters thesis, Concordia University.

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Genetic Algorithms (GAs) are stochastic search and optimization methods inspired by the mechanisms of natural adaptation. In the last two decades they have been researched and applied in a variety of areas. Currently GAs are used extensively in solving complex optimization problems with large but finite search space. This thesis studies two genetic algorithms applied to a trucking simulation problem where trucks travel among dealers in a country and transport commodities from producers to retailers and from retailers to consumers. Both trucks and retailers attempt to survive and make the most individual profits. Trucks and retailers evolve simultaneously in the simulation. Their evolution progress in two economy types is examined. The results show different effectiveness of these two algorithms in the two economy types.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Deng, Qixia
Pagination:ix, 97 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science and Software Engineering
Thesis Supervisor(s):Grogono, Peter
Identification Number:QA 76.623 D46 2003
ID Code:2025
Deposited By: Concordia University Library
Deposited On:27 Aug 2009 17:24
Last Modified:13 Jul 2020 19:51
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