Journal
EVOLUTIONARY COMPUTATION
Volume 23, Issue 1, Pages 161-185Publisher
MIT PRESS
DOI: 10.1162/EVCO_a_00134
Keywords
Evolutionary optimization; benchmarking; exploratory landscape analysis; BBOB test set; multidimensional scaling; consensus ranking
Funding
- Collaborative Research Center [SFB 823]
- Graduate School of Energy Efficient Production and Logistics
- Research Training Group Statistical Modelling of the German Research Foundation
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We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the best one? and the second one is: which algorithm should I use for my real-world problem? Both are connected and neither is easy to answer. We present a theoretical framework for designing and analyzing the raw data of such benchmark experiments. This represents a first step in answering the aforementioned questions. The 2009 and 2010 BBOB benchmark results are analyzed by means of this framework and we derive insight regarding the answers to the two questions. Furthermore, we discuss how to properly aggregate rankings from algorithm evaluations on individual problems into a consensus, its theoretical background and which common pitfalls should be avoided. Finally, we address the grouping of test problems into sets with similar optimizer rankings and investigate whether these are reflected by already proposed test problem characteristics, finding that this is not always the case.
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