A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, wh...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 23; číslo 1; s. 74 - 88
Hlavní autori:	Pan, Linqiang, He, Cheng, Tian, Ye, Wang, Handing, Zhang, Xingyi, Jin, Yaochu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Classification Computer simulation Evolutionary algorithms Evolutionary computation expensive many-objective optimization Fitness Genetic algorithms Linear programming Multiple objective analysis Neural networks Neurons Optimization Pareto dominance Prediction algorithms Signal processing algorithms State of the art surrogate-assisted evolutionary optimization Uncertainty
ISSN:	1089-778X, 1941-0026
On-line prístup:	Získať plný text
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