A Memetic Algorithm Assisted by an Adaptive Topology RBF Network and Variable Local Models for Expensive Optimization Problems

Advances in Evolutionary Algorithms ◽

10.5772/6137 ◽

2008 ◽

Author(s):

Yoel Tenne ◽

S.W. Armfiel

Keyword(s):

Memetic Algorithm ◽

Optimization Problems ◽

Local Models ◽

Rbf Network ◽

Expensive Optimization Problems ◽

Adaptive Topology ◽

Expensive Optimization

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Memetic algorithm using multi-surrogates for computationally expensive optimization problems

Soft Computing ◽

10.1007/s00500-006-0145-8 ◽

2006 ◽

Vol 11 (10) ◽

pp. 957-971 ◽

Author(s):

Zongzhao Zhou ◽

Yew Soon Ong ◽

Meng Hiot Lim ◽

Bu Sung Lee

Keyword(s):

Memetic Algorithm ◽

Optimization Problems ◽

Expensive Optimization Problems ◽

Computationally Expensive ◽

Expensive Optimization

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A Model-Assisted Memetic Algorithm for Expensive Optimization Problems

Nature-Inspired Algorithms for Optimisation - Studies in Computational Intelligence ◽

10.1007/978-3-642-00267-0_5 ◽

2009 ◽

pp. 133-169 ◽

Author(s):

Yoel Tenne

Keyword(s):

Memetic Algorithm ◽

Optimization Problems ◽

Expensive Optimization Problems ◽

Expensive Optimization

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Solving computationally expensive optimization problems using hybrid methods in parallel computing environments

10.2514/6.2000-4864 ◽

2000 ◽

Author(s):

John Eddy ◽

Kurt Hacker ◽

Kemper Lewis

Keyword(s):

Parallel Computing ◽

Optimization Problems ◽

Hybrid Methods ◽

Expensive Optimization Problems ◽

Computing Environments ◽

Computationally Expensive ◽

Expensive Optimization

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SAFE: Scale-Adaptive Fitness Evaluation Method for Expensive Optimization Problems

IEEE Transactions on Evolutionary Computation ◽

10.1109/tevc.2021.3051608 ◽

2021 ◽

pp. 1-1

Author(s):

Sheng-Hao Wu ◽

Zhi-Hui Zhan ◽

Jun Zhang

Keyword(s):

Evaluation Method ◽

Optimization Problems ◽

Fitness Evaluation ◽

Expensive Optimization Problems ◽

Expensive Optimization

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Evaluation of Teaching Learning Based Optimization with Focused Learning on Expensive Optimization Problems (CEC2017)

Smart Innovations in Communication and Computational Sciences - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-10-8968-8_37 ◽

2018 ◽

pp. 435-445

Author(s):

Remya Kommadath ◽

Prakash Kotecha

Keyword(s):

Optimization Problems ◽

Teaching Learning Based Optimization ◽

Expensive Optimization Problems ◽

Evaluation Of Teaching ◽

Expensive Optimization ◽

Teaching Learning

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Differential evolution with an ensemble of low-quality surrogates for expensive optimization problems

2016 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2016.7743781 ◽

2016 ◽

Author(s):

Mohanarangam Krithikaa ◽

Rammohan Mallipeddi

Keyword(s):

Differential Evolution ◽

Optimization Problems ◽

Expensive Optimization Problems ◽

Expensive Optimization

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A Mahalanobis Distance-Based Fitness Approximation Method for Estimation of Distribution Algorithms in Solving Expensive Optimization Problems

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) ◽

10.1109/smc.2019.8914652 ◽

2019 ◽

Author(s):

Yongsheng Liang ◽

Zhigang Ren ◽

Yang Yang ◽

An Chen ◽

Daofu Guo ◽

...

Keyword(s):

Approximation Method ◽

Mahalanobis Distance ◽

Optimization Problems ◽

Estimation Of Distribution Algorithms ◽

Estimation Of Distribution ◽

Expensive Optimization Problems ◽

Distribution Algorithms ◽

Expensive Optimization ◽

Fitness Approximation

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A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems

IEEE Transactions on Evolutionary Computation ◽

10.1109/tevc.2013.2248012 ◽

2014 ◽

Vol 18 (2) ◽

pp. 180-192 ◽

Author(s):

Bo Liu ◽

Qingfu Zhang ◽

Georges G. E. Gielen

Keyword(s):

Gaussian Process ◽

Evolutionary Algorithm ◽

Surrogate Model ◽

Optimization Problems ◽

Medium Scale ◽

Expensive Optimization Problems ◽

Expensive Optimization

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Bilevel-search particle swarm optimization for computationally expensive optimization problems

Soft Computing ◽

10.1007/s00500-021-06169-3 ◽

2021 ◽

Author(s):

Yuan Yan ◽

Qin Zhou ◽

Shi Cheng ◽

Qunfeng Liu ◽

Yun Li

Keyword(s):

Particle Swarm Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Swarm Optimization ◽

Expensive Optimization Problems ◽

Computationally Expensive ◽

Expensive Optimization

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Enhanced differential evolution using local Lipschitz underestimate strategy for computationally expensive optimization problems

Applied Soft Computing ◽

10.1016/j.asoc.2016.06.044 ◽

2016 ◽

Vol 48 ◽

pp. 169-181 ◽

Author(s):

Xiao-gen Zhou ◽

Gui-jun Zhang ◽

Xiao-hu Hao ◽

Dong-wei Xu ◽

Li Yu

Keyword(s):

Differential Evolution ◽

Optimization Problems ◽

Expensive Optimization Problems ◽

Computationally Expensive ◽

Expensive Optimization

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