Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Witteveen, Cees"'
The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA
Externí odkaz:
http://arxiv.org/abs/1904.02050
Autor:
Novák, Peter, Witteveen, Cees
Multi-context systems provide a powerful framework for modelling information-aggregation systems featuring heterogeneous reasoning components. Their execution can, however, incur non-negligible cost. Here, we focus on cost-complexity of such systems.
Externí odkaz:
http://arxiv.org/abs/1405.7295
Publikováno v:
In Journal of Computational and Applied Mathematics November 2019 360:157-169
Publikováno v:
In Artificial Intelligence September 2014 214:26-44
Publikováno v:
In Information and Computation March 2011 209(3):606-625
Autor:
van der Hoek, Wiebe, Witteveen, Cees
Publikováno v:
Studia Logica: An International Journal for Symbolic Logic, 2002 Feb 01. 70(1), 3-4.
Externí odkaz:
https://www.jstor.org/stable/20016378
Publikováno v:
AAAI, 2425-2426
STARTPAGE=2425;ENDPAGE=2426;TITLE=AAAI
STARTPAGE=2425;ENDPAGE=2426;TITLE=AAAI
In case of a plan failure, plan-repair is a more promising solution than replanning from scratch. The effectiveness of plan-repair depends on knowledge of which plan action failed and why. Therefore, in this paper, we propose an Extended Spectrum Bas
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Artificial Intelligence 2002 142(2):121-145
Publikováno v:
GECCO '17 : Genetic and Evolutionary Computation Conference, 537-544
STARTPAGE=537;ENDPAGE=544;TITLE=GECCO '17 : Genetic and Evolutionary Computation Conference
GECCO
STARTPAGE=537;ENDPAGE=544;TITLE=GECCO '17 : Genetic and Evolutionary Computation Conference
GECCO
The recently introduced Multi-Objective Gene-pool Optimal Mixing Evolutionary Algorithm (MO-GOMEA) exhibits excellent scalability in solving a wide range of challenging discrete multi-objective optimization problems. In this paper, we address scalabi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f54e15405522ad2fabaac7f27e7d820
https://ir.cwi.nl/pub/26582
https://ir.cwi.nl/pub/26582