A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm

When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the region of interest of the Pareto optimal front....

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of global optimization Ročník 62; číslo 1; s. 101 - 129
Hlavní autoři:	Ruiz, Ana Belén, Saborido, Rubén, Luque, Mariano
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.05.2015 Springer Springer Nature B.V
Témata:	Algorithms Analysis Classification Computer Science Decision making Evolutionary algorithms Genetic algorithms Mathematical analysis Mathematical programming Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Optimization algorithms Pareto optimality Pareto optimum Population Preferences Real Functions Studies Vectors (mathematics) Weighting United States > US Pareto optimal solutions Achievement scalarizing function Multiobjective optimization Evolutionary algorithm Reference point approach
ISSN:	0925-5001, 1573-2916
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!