Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2015

Abstract

Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We apply our approach on a Simulated Annealing-Tabu Search (SA-TS) hybrid algorithm for solving the Quadratic Assignment Problem (QAP) as well as an Iterated Local Search (ILS) on the Travelling Salesman Problem (TSP). We also generate a portfolio of parameter configurations using best-of-breed parameter tuning approaches directly for the comparison purpose. Experimental results show that our approach lead to improvements over best-of-breed parameter tuning approaches.

Keywords

Artificial intelligence, Combinatorial optimization, Design of experiments, OptimizationParameter estimation, Problem solving, Simulated annealing, Tabu search, Traveling salesman problem

Discipline

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

Research Areas

Intelligent Systems and Optimization

Publication

Algorithm Configuration: Papers from the 2015 AAAI Workshop: January 25-29, Austin, TX

First Page

2

Last Page

8

ISBN

9781577357124

Identifier

AAAI Press

Publisher

AAAI Press

City or Country

Palo Alto, CA

Copyright Owner and License

Publisher

Additional URL

https://www.aaai.org/ocs/index.php/WS/AAAIW15/paper/viewFile/10107/10121

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