Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Viviane Grunert da Fonseca"'
Autor:
Sven Deutschmann, Mousumi Paul, Marja Claassen-Willemse, Jonas van den Berg, Pieta IJzerman-Boon, Viviane Grunert da Fonseca, Ellen Brunbech, Lynn Johnson, Chris Knutsen, Lucile Plourde, Joanny Salvas, Philip Villari, Lisa Wysocki, Margit Franz-Riethdorf
Publikováno v:
PDA Journal of Pharmaceutical Science and Technology. :pdajpst.2021.012672
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fde1305a7ffe11c5c7a01d6c056d3235
https://doi.org/10.5731/pdajpst.2021.012672
https://doi.org/10.5731/pdajpst.2021.012672
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642355325
Artificial Evolution
Artificial Evolution
This paper investigates the relationship between the covered fraction, completeness, and (weighted) hypervolume indicators for assessing the quality of the Pareto-front approximations produced by multiobjective optimizers. It is shown that these unar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::028c5f5de6604683794f0c25d0945384
https://doi.org/10.1007/978-3-642-35533-2_3
https://doi.org/10.1007/978-3-642-35533-2_3
Publikováno v:
Experimental Methods for the Analysis of Optimization Algorithms ISBN: 9783642025372
Experimental Methods for the Analysis of Optimization Algorithms
Experimental Methods for the Analysis of Optimization Algorithms
This chapter presents the attainment-function approach to the assessment and comparison of stochastic multiobjective optimizer (MO) performance. Since the random outcomes of stochastic MOs, such as multiobjective evolutionary algorithms, are sets of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc4afab85d2d8e00ec66763ad61049e0
https://doi.org/10.1007/978-3-642-02538-9_5
https://doi.org/10.1007/978-3-642-02538-9_5
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540249832
EMO
EMO
The attainment function has been proposed as a measure of the statistical performance of stochastic multiobjective optimisers which encompasses both the quality of individual non-dominated solutions in objective space and their spread along the trade
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e859cdf9ee565e8e34e92e0d1520387
https://doi.org/10.1007/978-3-540-31880-4_18
https://doi.org/10.1007/978-3-540-31880-4_18
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540417453
EMO
EMO
The performance of stochastic optimisers can be assessed experimentally on given problems by performing multiple optimisation runs, and analysing the results. Since an optimiser may be viewed as an estimator for the (Pareto) minimum of a (vector) fun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b4a824d7c4a0cce74cd3a1ce9c8f4fc
https://doi.org/10.1007/3-540-44719-9_15
https://doi.org/10.1007/3-540-44719-9_15