Výsledky vyhledávání - "real-valued optimization"

Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization: Including Real-World Medical Applications

Autor: Bouter, P.A.

In recent years, the use of Artificial Intelligence (AI) has become prevalent in a large number of societally relevant, real-world problems, e.g., in the domains of engineering and health care. The field of Evolutionary Computation (EC) can be consid

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http://resolver.tudelft.nl/uuid:0e03913c-898e-4392-8de5-072a7ead7fd6

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Hybridizing Hypervolume-Based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation

Autor: Damy M. F. Ha, Timo M. Deist, Peter A. N. Bosman

Publikováno v: Lecture Notes in Computer Science ISBN: 9783031147203

Evolutionary algorithms (EAs) are well-known to be well suited for multi-objective (MO) optimization. However, especially in the case of real-valued variables, classic domination-based approaches are known to lose selection pressure when approaching

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::983b5cdf82395b5fe75065998539a11c
https://doi.org/10.1007/978-3-031-14721-0_13

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Achieving highly scalable evolutionary real-valued optimization by exploiting partial evaluations

Autor: Anton Bouter, Tanja Alderliesten, Peter A. N. Bosman

Publikováno v: Evolutionary Computation, 29(1), 129-155. MIT Press Journals
Evolutionary Computation, 29(1), 129-155
Evolutionary Computation, 29(1), 129-155. MIT PRESS

It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regar

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85101967204&origin=inward

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Fitness-based linkage learning in the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm

Autor: Anton Bouter, Chantal Olieman, Peter A. N. Bosman

Publikováno v: IEEE Transactions on Evolutionary Computation, 25(2), 358-370
IEEE Transactions on Evolutionary Computation, 25(2)

The recently introduced real-valued gene-pool optimal mixing evolutionary algorthm (RV-GOMEA) has been shown to be among the state of the art for solving gray-box optimization problems where partial evaluations can be leveraged. A core strength is it

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https://ir.cwi.nl/pub/30301

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Spatial redistribution of irregularly-spaced Pareto fronts for more intuitive navigation and solution selection

Autor: Bouter, Anton, Pirpinia, Kleopatra, Alderliesten, Tanja, Bosman, Peter A. N.

Publikováno v: GECCO (Companion)
GECCO '17 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1697-1704
STARTPAGE=1697;ENDPAGE=1704;TITLE=GECCO '17 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion

A multi-objective optimization approach is often followed by an a posteriori decision-making process, during which the most appropriate solution of the Pareto set is selected by a professional in the field. Conventional visualization methods do not c

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https://doi.org/10.1145/3067695.3082555

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Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm

Autor: Bouter, Anton, Alderliesten, Tanja, Witteveen, Cees, Bosman, Peter A. N.

Publikováno v: GECCO '17 : Genetic and Evolutionary Computation Conference, 705-712
STARTPAGE=705;ENDPAGE=712;TITLE=GECCO '17 : Genetic and Evolutionary Computation Conference
GECCO

The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) has been shown to be among the state-of-the-art for solving discrete optimization problems. Key to the success of GOMEA is its ability to efficiently exploit the linkage

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https://pure.amc.nl/en/publications/exploiting-linkage-information-in-realvalued-optimization-with-the-realvalued-genepool-optimal-mixing-evolutionary-algorithm(7dd15cc9-5994-4238-8afa-e7ce2c656930).html

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