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4.0
Jun 29, 2018
06/18
by
Dimo Brockhoff; Tea Tušar; Dejan Tušar; Tobias Wagner; Nikolaus Hansen; Anne Auger
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This document details the rationales behind assessing the performance of numerical black-box optimizers on multi-objective problems within the COCO platform and in particular on the biobjective test suite bbob-biobj. The evaluation is based on a hypervolume of all non-dominated solutions in the archive of candidate solutions and measures the runtime until the hypervolume value succeeds prescribed target values.
Topics: Neural and Evolutionary Computing, Computing Research Repository
Source: http://arxiv.org/abs/1605.01746
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3.0
Jun 29, 2018
06/18
by
Nikolaus Hansen; Anne Auger; Olaf Mersmann; Tea Tusar; Dimo Brockhoff
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COCO is a platform for Comparing Continuous Optimizers in a black-box setting. It aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. We present the rationals behind the development of the platform as a general proposition for a guideline towards better benchmarking. We detail underlying fundamental concepts of COCO such as its definition of a problem, the idea of instances, the relevance of target values, and...
Topics: Machine Learning, Artificial Intelligence, Numerical Analysis, Computing Research Repository,...
Source: http://arxiv.org/abs/1603.08785
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6.0
Jun 29, 2018
06/18
by
Nikolaus Hansen; Anne Auger; Dimo Brockhoff; Dejan Tušar; Tea Tušar
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We present an any-time performance assessment for benchmarking numerical optimization algorithms in a black-box scenario, applied within the COCO benchmarking platform. The performance assessment is based on runtimes measured in number of objective function evaluations to reach one or several quality indicator target values. We argue that runtime is the only available measure with a generic, meaningful, and quantitative interpretation. We discuss the choice of the target values, runlength-based...
Topics: Neural and Evolutionary Computing, Computing Research Repository
Source: http://arxiv.org/abs/1605.03560
6
6.0
Jun 29, 2018
06/18
by
Tea Tusar; Dimo Brockhoff; Nikolaus Hansen; Anne Auger
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The bbob-biobj test suite contains 55 bi-objective functions in continuous domain which are derived from combining functions of the well-known single-objective noiseless bbob test suite. Besides giving the actual function definitions and presenting their (known) properties, this documentation also aims at giving the rationale behind our approach in terms of function groups, instances, and potential objective space normalization.
Topics: Artificial Intelligence, Neural and Evolutionary Computing, Computing Research Repository
Source: http://arxiv.org/abs/1604.00359
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4.0
Jun 29, 2018
06/18
by
Nikolaus Hansen; Tea Tusar; Olaf Mersmann; Anne Auger; Dimo Brockhoff
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We present a budget-free experimental setup and procedure for benchmarking numericaloptimization algorithms in a black-box scenario. This procedure can be applied with the COCO benchmarking platform. We describe initialization of and input to the algorithm and touch upon therelevance of termination and restarts.
Topics: Artificial Intelligence, Neural and Evolutionary Computing, Computing Research Repository
Source: http://arxiv.org/abs/1603.08776
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3.0
Jun 30, 2018
06/18
by
Bilel Derbel; Dimo Brockhoff; Arnaud Liefooghe; Sébastien Verel
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Recently, there has been a renewed interest in decomposition-based approaches for evolutionary multiobjective optimization. However, the impact of the choice of the underlying scalarizing function(s) is still far from being well understood. In this paper, we investigate the behavior of different scalarizing functions and their parameters. We thereby abstract firstly from any specific algorithm and only consider the difficulty of the single scalarized problems in terms of the search ability of a...
Topics: Computing Research Repository, Artificial Intelligence
Source: http://arxiv.org/abs/1409.5752
