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

By Bo Liu, Qingfu Zhang, G. Gielen. IEEE Transactions on Evolutionary Computation, 2014

512 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/cb075e429f05292f426a72bfc5a482f51d08caec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yaochu Jin
2. Handing Wang
3. Ran Cheng
4. Tinkle Chugh
5. Xing-yi Zhang
6. Kaisa Miettinen
7. Ye Tian
8. Kay Chen Tan

In Computational Theory and Mathematics

1. Yaochu Jin
2. Handing Wang
3. Ran Cheng
4. Tinkle Chugh
5. Xing-yi Zhang
6. Kaisa Miettinen
7. Ye Tian
8. John James Doherty

In Mechanical Engineering

1. Handing Wang
2. John James Doherty
3. Yaochu Jin
4. Kaisa Miettinen
5. Chaoli Sun
6. Tinkle Chugh

Citations in the corpus

• Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, methodology): “Infill criteria, including expected improvement [32], lower confidence bound (LCB) [33], [34], and probability of improvement [35], are most widely used in Kriging or Gaussian process assisted EAs.”
• Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, methodology): “From these results, we can see that CAL-SAPSO significantly outperforms LCB and ExI, while CAL-SAPSO-WOU cannot significantly outperform LCB and ExI.”
• Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019, background): “Existing infill criteria include the expected improvement (ExI) [51], [52], probability of improvement [53], and lower confidence bound [54].”
• A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020, background): “In the last decade, data-driven surrogate-assisted EAs (SAEAs) [6] have found many successful real-world applications, such as aerodynamic design [7], [8]; microwave design [9]; furnace design [10]; and circuit design [11].”
• A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015, background): “pletely different from those in surrogate-assisted evolutionary algorithms (SAEAs) [53], although some work on SAEAs also adopted the GP as surrogates [54].”
• A Survey on Evolutionary Constrained Multiobjective Optimization (2023, background): “3) Handling Computationally Expensive CMOPs: Some practical optimization problems require large computing resources and an amount of time for function evaluation [186], [187], such as computational electromagnetics [188] and computational fluid dynamics [189].”
• Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems (2017, methodology): “[39] to assist differential evolution to solve computationally expensive optimization problems, in which dimension reduction techniques were utilized to reduce the dimension of the GP surrogate model.”