Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Brockhoff, Dimo"'
We present a framework to build a multiobjective algorithm from single-objective ones. This framework addresses the $p \times n$-dimensional problem of finding p solutions in an n-dimensional search space, maximizing an indicator by dynamic subspace
Externí odkaz:
http://arxiv.org/abs/1904.08823
Autor:
Elhara, Ouassim, Varelas, Konstantinos, Nguyen, Duc, Tusar, Tea, Brockhoff, Dimo, Hansen, Nikolaus, Auger, Anne
The bbob-largescale test suite, containing 24 single-objective functions in continuous domain, extends the well-known single-objective noiseless bbob test suite, which has been used since 2009 in the BBOB workshop series, to large dimension. The core
Externí odkaz:
http://arxiv.org/abs/1903.06396
In this paper we analyze theoretical properties of bi-objective convex-quadratic problems. We give a complete description of their Pareto set and prove the convexity of their Pareto front. We show that the Pareto set is a line segment when both Hessi
Externí odkaz:
http://arxiv.org/abs/1812.00289
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 f
Externí odkaz:
http://arxiv.org/abs/1605.03560
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 hype
Externí odkaz:
http://arxiv.org/abs/1605.01746
Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, like well-understood Pareto sets and Pareto fronts of various shapes, most of the currently u
Externí odkaz:
http://arxiv.org/abs/1604.00359
We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting. COCO aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. The pl
Externí odkaz:
http://arxiv.org/abs/1603.08785
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 alg
Externí odkaz:
http://arxiv.org/abs/1603.08776
Autor:
Brockhoff, Dimo
Zugl.: Zürich, Techn. Hochsch., Diss., 2009
Externí odkaz:
http://d-nb.info/997752726/04