Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Dirk Thierens"'
Publikováno v:
GECCO 2022-Proceedings of the 2022 Genetic and Evolutionary Computation Conference
Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue of linkage (or variable interaction) learning. This requires, however, that the linkage model can capture the exploitable structure of a problem. Usually, a single type of l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e799f076589e70d3eb5795cb9ad61b33
https://ir.cwi.nl/pub/31980
https://ir.cwi.nl/pub/31980
Publikováno v:
GECCO Companion
Mixed-integer optimization, which focuses on problems where discrete and continuous variables exist simultaneously, is a well-known and challenging area for search algorithms. Mixed-integer optimization problems are especially difficult in a black-bo
Publikováno v:
Natural Computing Series ISBN: 9783030795528
The aim of multimodal optimization (MMO) is to obtain all global optima of an optimization problem. In this chapter, we introduce a general framework for two-phase MMO evolutionary algorithms (EAs), in which different high-fitness regions (niches) ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16fd2ae4b52abb3da44e8f968e10dbcf
https://doi.org/10.1007/978-3-030-79553-5_8
https://doi.org/10.1007/978-3-030-79553-5_8
Autor:
Dirk Thierens, Rogier Hans Wuijts
Publikováno v:
GECCO
The Traveling Thief Problem (TTP) is a relatively new benchmark problem created to study problems which consist of interdependent subproblems. In this paper we investigate what the fitness landscape characteristics are of some smaller instances of th
Publikováno v:
GECCO
Bayesian networks (BNs) are probabilistic graphical models which are widely used for knowledge representation and decision making tasks, especially in the presence of uncertainty. Finding or learning the structure of BNs from data is an NP-hard probl
Publikováno v:
GECCO 2018-Proceedings of the 2018 Genetic and Evolutionary Computation Conference, 857-864
STARTPAGE=857;ENDPAGE=864;TITLE=GECCO 2018-Proceedings of the 2018 Genetic and Evolutionary Computation Conference
STARTPAGE=857;ENDPAGE=864;TITLE=GECCO 2018-Proceedings of the 2018 Genetic and Evolutionary Computation Conference
Model-based evolutionary algorithms (EAs) adapt an underlying search model to features of the problem at hand, such as the linkage between problem variables. The performance of EAs often deteriorates as multiple modes in the fitness landscape are mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5acc3d496592c02d5d9052c110918b21
https://pure.amc.nl/en/publications/realvalued-evolutionary-multimodal-optimization-driven-by-hillvalley-clustering(ac1de559-65dd-4950-b369-0710ced57ed2).html
https://pure.amc.nl/en/publications/realvalued-evolutionary-multimodal-optimization-driven-by-hillvalley-clustering(ac1de559-65dd-4950-b369-0710ced57ed2).html
Publikováno v:
Parallel Problem Solving from Nature – PPSN XV ISBN: 9783319992525
PPSN (1)
PPSN (1)
The recently introduced permutation Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) has shown to be an effective Model Based Evolutionary Algorithm (MBEA) for permutation problems. So far, permutation GOMEA has only been used in the context o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e3ae56db7b640653a94c1a8bd61ddcc
https://doi.org/10.1007/978-3-319-99253-2_12
https://doi.org/10.1007/978-3-319-99253-2_12
Autor:
Peter A. N. Bosman, Dirk Thierens
Publikováno v:
GECCO 2019 Companion-Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, 806-836
STARTPAGE=806;ENDPAGE=836;TITLE=GECCO 2019 Companion-Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
GECCO (Companion)
STARTPAGE=806;ENDPAGE=836;TITLE=GECCO 2019 Companion-Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
GECCO (Companion)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6404c1c29824f8b57b56a2e587c9251
https://ir.cwi.nl/pub/26540
https://ir.cwi.nl/pub/26540
Publikováno v:
Evolutionary Computation, 26(1), 117-143
Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Al