Journal article
Discovering the suitability of optimisation algorithms by learning from evolved instances
K Smith-Miles, J van Hemert
Annals of Mathematics and Artificial Intelligence | SPRINGER | Published : 2011
Abstract
The suitability of an optimisation algorithm selected from within an algorithm portfolio depends upon the features of the particular instance to be solved. Understanding the relative strengths and weaknesses of different algorithms in the portfolio is crucial for effective performance prediction, automated algorithm selection, and to generate knowledge about the ideal conditions for each algorithm to influence better algorithm design. Relying on well-studied benchmark instances, or randomly generated instances, limits our ability to truly challenge each of the algorithms in a portfolio and determine these ideal conditions. Instead we use an evolutionary algorithm to evolve instances that are..
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Funding Acknowledgements
We are grateful to the participants of the Learning and Intelligent Optimization (LION) workshop, held in Venice January 18th-22nd 2010, for useful discussions on this work, and the topic of performance prediction. We also acknowledge the support of Xin Yu Lim (Oxford University, UK) for her assistance with developing the MATLAB code for the TSP features.