Screening the parameters affecting heuristic performance

Authors:
Enda Ridge;Daniel Kudenko
Affiliations:
The University of York, York, United Kingdom;The University of York, York, United Kingdom
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

Citing 2
Cited 1

Ant Colony Optimization

Ant Colony Optimization
Analyzing heuristic performance with response surface models: prediction, optimization and robustness

Proceedings of the 9th annual conference on Genetic and evolutionary computation

Analyzing heuristic performance with response surface models: prediction, optimization and robustness

Proceedings of the 9th annual conference on Genetic and evolutionary computation

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Abstract

This research screens the tuning parameters of a combinatorial optimization heuristic. Specifically, it presents a Design of Experiments (DOE) approach that uses a Fractional Factorial Design to screen the tuning parameters of Ant Colony System (ACS) for the Travelling Sales person problem. Screening is a preliminary step towards building a full Response Surface Model (RSM) [2]. It identifies parametersthat have little influence on performance and can be omittedfrom the RSM design. This reduces the complexity andexpense of the RSM design. 10 algorithm parameters and 2 problem characteristics are considered. Open questionson the effect of 3 parameters on performance are answered.A further parameter, sometimes assumed important, was shown to have no effect on performance. A new problem characteristic that effects performance was identified. A full version of this paper is available [3].