Ieee Transactions on Systems, Man and Cybernetics 1 Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization

By Zongzhao Zhou, Yew-Soon Ong, Prasanth B. Nair, Andy J. Keane, Kai Yew Lum

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Citations in the corpus

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, methodology): “Generally, the models are trained from a small number of expensive ﬁtness evaluations (i.e., the samples with evaluated objective values), and then the trained models are used as the ﬁtness predictors in the evolutionary search to accelerate the optimization process [41].”
Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems (2017, methodology): “[26], [51] proposed a hierarchical surrogate-assisted evolutionary algorithm in which a GP was used as a global surrogate to prescreen promising individuals and an RBF network was utilized as the local surrogate to assist the trust-region enabled gradient-based search strategy to accelerate converge”
A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems (2014, methodology): “As shown in Table I, GS-SOMA has been tested on F5, F8, F11, F13, and F14 in [1], SAGAGLS on F4, F7 and F10 in [11], and MAES on F1 and F7 in [10].”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, unknown): “Some key promising directions in developing on-line data-driven EAs include 1) using ensemble of surrogate models [51, 102, 85, 30], 2) enhancing the convergence by using a combination of local and global surrogate models [61, 98, 119, 88, 111], 3) decreasing the computational complexity of the prob”
Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, methodology): “To reduce the needed number of expensive function evaluations, surrogate-assisted evolutionary algorithms (SAEAs) [22], [8], [21] can be used, where the main idea is to use one [23], [24] or multiple surrogates [25], [26], [27] to approximate the expensive function evaluation globally or locally [28”
Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, methodology): “Another idea is to use a combination of global and local surrogate models in which the global model is used to smoothen out the local optimums while the local ones are utilized for exploiting the local details of the ﬁtness landscape [27]–[29].”
Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation (2012, methodology): “In addition, hierarchical models (Kampolis et al., 2007; Zhou et al., 2007; Lim et al., 2010) which pick up the meta-modeling approach and expand it to multiple models of different complexity are a straightforward approach and a matter of current research.”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, methodology): “The synergy of online global and local surrogate in the memetic search was also investigated in [11].”
Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, background): “D single-objective benchmark problems, the PoI-based SAGA with global and local search strategy (SAGA-GLS) [27] costs 6000 exact FEs on 20-”
Surrogate-assisted evolutionary computation: Recent advances and future challenges (2011, methodology): “To overcome this difficulty, the surrogate can be built up in a new space of a lower dimension using dimension reduction techniques [49,50].”