Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

2008

Abstract

In this work, we liken the solving of combinatorial optimization problems under a prescribed computational budget as hunting for oil in an unexplored ground. Using this generic model, we instantiate an iterative deepening genetic annealing (IDGA) algorithm, which is a variant of memetic algorithms. Computational results on the traveling salesman problem show that IDGA is more effective than standard genetic algorithms or simulated annealing algorithms or a straightforward hybrid of them. Our model is readily applicable to solve other combinatorial optimization problems.

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management

Volume

144

First Page

169

Last Page

184

ISBN

9783540690245

Identifier

10.1007/978-3-540-69390-1_9

Publisher

Springer Verlag

City or Country

Berlin

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1007/978-3-540-69390-1_9

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