Surrogate-assisted evolutionary computation: Recent advances and future challenges

By Yaochu Jin. 2011

1,344 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, background): “In fact, the optimization problem in EDLs is a type of computationally expensive problems, which is usually solved by surrogate-assisted evolutionary algorithms (SAEAs) [36],”
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, methodology): “To address this problem, several surrogate-assisted evolutionary algorithms [15] have been proposed in the literature to obtain solutions in a few function evaluations.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, neutral): “For more information one can refer to recent review studies (Queipo et al. 2005; Forrester and Keane 2009; Shan and Wang 2010b; Parr et al. 2010; Jin 2011; Martins and Lambe 2013; Haftka et al. 2016) and the references therein.”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, background): “In this context, a research line with a long history in metaheuristic optimization is the use of machine learning surrogates [144].”
On Using Surrogates with Genetic Programming (2015, methodology): “The data used to learn the surrogate model can come from an experimental design conducted before the EA, but usually is learned and adopted online during the run (Jin, 2005, 2011; Jin and Branke, 2005).”
Model-based methods for continuous and discrete global optimization (2017, background): “A good introduction and a comprehensive survey is presented by Jin [27, 28].”
A survey on evolutionary computation for complex continuous optimization (2021, methodology): “To solve this problem and reduce the optimization difficulties, approximation methods have been widely researched (Shan and Wang 2010; Tenne and Goh 2010; Jin 2011; Jin et al.”
Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, methodology): “Evolutionary optimization of such problems will not be able to afford thousands of function evaluations as required by most existing EAs.”
A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, background): “One popular approach to expensive optimization problems is to introduce computationally efficient surrogates for approximating the expensive fitness evaluations [25], [26].”
Recent advances in differential evolution - An updated survey (2016, methodology): “As a remedy, surrogateassisted evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in EAs [91].”
Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, background): “To overcome this barrier, surrogate-assisted EAs (SAEAs) have been developed, where part of the expensive fitness evaluations are replaced by computationally cheap approximate models, often known as meta-models or surrogates [5], [8].”
Efficient global optimization for combinatorial problems (2014, background): “Combinatorial surrogate models are a relatively new research topic [13].”
Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, methodology): “This type of data can be directly used to train surrogate models to approximate the objective and / or constraint functions, which has been the main focus in SAEAs [45, 12].”
Bio-inspired computation: Where we stand and what's next (2019, background): “computationally expensive objectives are replaced by cheaper regression models built on a few evaluated individuals [430].”
Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems (2017, methodology): “2) Local-surrogate assisted metaheuristic algorithms Generally, it is difficult, in particular for highdimensional problems to build a reliable global surrogate model due to the “curse of dimensionality” [41], [18].”
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming (2018, positive): “…a) Sequential SMBO generates new candidate solutions by performing a search procedure on the surrogate model, e.g., as described for the Eﬃcient Global Optimization (EGO) algorithm by Jones et al. [8]. b) Approaches that utilize surrogates to assist an EA (SAEA), e.g., as described by Jin [9].”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, background): “evolutionary and swarm algorithms are also under-researched [183], [184].”