Surrogate-assisted evolutionary computation: Recent advances and future challenges

Jin, Yaochu

doi:10.1016/j.swevo.2011.05.001

Cited by 1,168 publications

(560 citation statements)

References 60 publications

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“…Several books and literature reviews have described the advances of surrogate-based optimization in recent years (e.g., Jones, 2001;Ong et al, 2005;Jin, 2011;Koziel and Leifsson, 2013;Wang et al, 2014). Surrogate-based optimization has been applied to economics, robotics, chemistry, physics, civil and environmental engineering, computational fluid dynamics, aerospace designs, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Gong

Duan

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ∼ 10 5 -10 6 ). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM -the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regionalscale LSMs, atmospheric models and climate models.

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Section: Introductionmentioning

confidence: 99%

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Gong

Duan

et al. 2015

Hydrol. Earth Syst. Sci.

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“…Surrogate-assisted GP can be designed to effectively use these models in the algorithm. Currently, surrogate models are only used as the pre-selection strategy [62]. Other applications of surrogate models in GP can be also investigated in future studies (e.g.…”

Section: Surrogate-assisted Modelsmentioning

confidence: 99%

Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

217

101

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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“…Increasingly EAs are being used for problems where evaluating each population member over many generations would take too long to permit effective evolution given the resources available. A range of approaches, collectively known as surrogate models, are being developed that use computationally cheaper models in place of full fitness evaluations, and refine those models via occasional full evaluations of targeted individuals 72,73,74,75 .…”

Section: Automated Design and Tuning Of Easmentioning

confidence: 99%

From evolutionary computation to the evolution of things

Eiben¹,

Smith

2015

Nature

396

190

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Surrogate-assisted evolutionary computation: Recent advances and future challenges

Cited by 1,168 publications

References 60 publications

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Multi-objective parameter optimization of common land model using adaptive surrogate modeling

Genetic programming for production scheduling: a survey with a unified framework

From evolutionary computation to the evolution of things

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