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Towards objective measures of algorithm performance across instance space
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AI
DOI：10.1016/j.cor.2013.11.015.png)
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Abstract
En 中文
This paper tackles the difficult but important task of objective algorithm performance assessment for optimization. Rather than reporting average performance of algorithms across a set of chosen instances, which may bias conclusions, we propose a methodology to enable the strengths and weaknesses of different optimization algorithms to be compared across a broader instance space. The results reported in a recent Computers and Operations Research paper comparing the performance of graph coloring heuristics are revisited with this new methodology to demonstrate (i) how pockets of the instance space can be found where algorithm performance varies significantly from the average performance of an algorithm; (ii) how the properties of the instances can be used to predict algorithm performance on previously unseen instances with high accuracy; and (iii) how the relative strengths and weaknesses of each algorithm can be visualized and measured objectively. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Comparative analysis
Heuristics
Graph coloring
Algorithm selection
Performance prediction
Journal
C
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4.3
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