A novel genetic algorithm based method for solving continuous nonlinear optimization problems through subdividing and labeling

[Display omitted] •We introduce a novel algorithm (SLGA) to solve continuous nonlinear optimization problems.•SLGA defines an integer label on a polytope built on a n-dimensional search space.•SLGA applies a crossover operator to approach the optimum by reducing the search space.•New population is g...

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Vydané v:	Measurement : journal of the International Measurement Confederation Ročník 115; s. 27 - 38
Hlavní autori:	Esmaelian, Majid, Tavana, Madjid, Santos-Arteaga, Francisco J., Vali, Masoumeh
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Elsevier Ltd 01.02.2018 Elsevier Science Ltd
Predmet:	Combinatorial analysis Continuity (mathematics) Evolutionary algorithms Fitness function Genetic algorithm (GA) Genetic algorithms Iterative methods Labeling Mathematical analysis Nonlinear equations Nonlinear optimization Operators (mathematics) Optimization Optimization algorithms Studies Subdividing labeling Genetic algorithm (GA) Subdividing labeling Fitness function Nonlinear optimization
ISSN:	0263-2241, 1873-412X
On-line prístup:	Získať plný text
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