Zobrazeno 1 - 10
of 311
pro vyhledávání: '"Carsten Witt"'
Autor:
Sven Herrmann
Publikováno v:
ACM SIGACT News. 44:22-26
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94ba735dcae9b5c845af9a1d80d3ebc1
https://doi.org/10.1145/2491533.2491540
https://doi.org/10.1145/2491533.2491540
Autor:
Witt, Carsten
Publikováno v:
Proceedings of the 12th Annual Conference Companion: Genetic & Evolutionary Computation; 7/ 7/2010, p2795-2840, 46p
Autor:
Schmidt, Volker
Publikováno v:
Arbeit und Recht, 1988 Aug 01. 36(8), 253-254.
Externí odkaz:
https://www.jstor.org/stable/24021060
Autor:
Amirhossein Rajabi, Carsten Witt
Publikováno v:
GECCO
Rajabi, A & Witt, C 2020, Self-adjusting evolutionary algorithms for multimodal optimization . in Proceedings of the 2020 Genetic and Evolutionary Computation Conference . Association for Computing Machinery, pp. 1314-1322, 2020 Genetic and Evolutionary Computation Conference, Cancun, Mexico, 08/07/2020 . https://doi.org/10.1145/3377930.3389833
Rajabi, A & Witt, C 2020, Self-adjusting evolutionary algorithms for multimodal optimization . in Proceedings of the 2020 Genetic and Evolutionary Computation Conference . Association for Computing Machinery, pp. 1314-1322, 2020 Genetic and Evolutionary Computation Conference, Cancun, Mexico, 08/07/2020 . https://doi.org/10.1145/3377930.3389833
Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms can provably outperform static settings in evolutionary algorithms for binary search spaces. However, the vast majority of these studies focuses on unimodal functi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c3fe6e3fcb5476c3a63d55d1a9ce80e
https://doi.org/10.1007/s00453-022-00933-z
https://doi.org/10.1007/s00453-022-00933-z
Publikováno v:
Algorithmica. 84:1603-1658
Recent progress in the runtime analysis of evolutionary algorithms (EAs) has allowed the derivation of upper bounds on the expected runtime of standard steady-state genetic algorithms (GAs). These upper bounds have shown speed-ups of the GAs using cr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::131d70ce67ef7da357e3f6affbf79f67
https://doi.org/10.1007/s00453-021-00893-w
https://doi.org/10.1007/s00453-021-00893-w
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference.
The compact genetic algorithm (cGA) is an non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad88c35aa1f1a98f976c21822e18ea52
https://doi.org/10.1145/3512290.3528776
https://doi.org/10.1145/3512290.3528776
Autor:
Carsten Witt, Per Kristian Lehre
Publikováno v:
Lehre, P K & Witt, C 2021, ' Tail bounds on hitting times of randomized search heuristics using variable drift analysis ', Combinatorics Probability and Computing, vol. 30, no. 4, pp. 550–569 . https://doi.org/10.1017/S0963548320000565
Drift analysis is one of the state-of-the-art techniques for the runtime analysis of randomized search heuristics (RSHs) such as evolutionary algorithms (EAs), simulated annealing, etc. The vast majority of existing drift theorems yield bounds on the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f1f6dc17464ae43649925dd8a73b98f
https://doi.org/10.1017/s0963548320000565
https://doi.org/10.1017/s0963548320000565
Publikováno v:
GECCO
Neumann, F, Pourhassan, M & Witt, C 2019, Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraint . in Proceedings of the 2019 Genetic and Evolutionary Computation Conference . Association for Computing Machinery, GECCO 2019-Proceedings of the 2019 Genetic and Evolutionary Computation Conference, pp. 1506-1514, 2019 Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13/07/2019 . https://doi.org/10.1145/3321707.3321722
Neumann, F, Pourhassan, M & Witt, C 2019, Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraint . in Proceedings of the 2019 Genetic and Evolutionary Computation Conference . Association for Computing Machinery, GECCO 2019-Proceedings of the 2019 Genetic and Evolutionary Computation Conference, pp. 1506-1514, 2019 Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13/07/2019 . https://doi.org/10.1145/3321707.3321722
In the last decade remarkable progress has been made in development of suitable proof techniques for analysing randomised search heuristics. The theoretical investigation of these algorithms on classes of functions is essential to the understanding o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c66ab13925482066db6fac0a44188fc
https://doi.org/10.1007/s00453-020-00779-3
https://doi.org/10.1007/s00453-020-00779-3
Autor:
Frank Neumann, Carsten Witt
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031147203
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fad69081aa213b4cb6fb69fb10920cb
https://doi.org/10.1007/978-3-031-14721-0_38
https://doi.org/10.1007/978-3-031-14721-0_38
