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Evolutionary Model Type Selection for Global Surrogate Modeling

Authors:

Filip De TurckAuthors Info & Claims

The Journal of Machine Learning Research, Volume 10

Pages 2039 - 2078

Published: 01 December 2009 Publication History

Abstract

Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist (Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm (heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type.

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 10, Issue

12/1/2009

2936 pages

ISSN:1532-4435

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Published: 01 December 2009

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https://dl.acm.org/doi/10.1007/978-3-030-50426-7_4
Giabbanelli PSon YHaas P(2019)Solving challenges at the interface of simulation and big data using machine learningProceedings of the Winter Simulation Conference10.5555/3400397.3400444(572-583)Online publication date: 8-Dec-2019
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Mehmani AChowdhury SMeinrenken CMessac A(2018)Concurrent surrogate model selection (COSMOS)Structural and Multidisciplinary Optimization10.1007/s00158-017-1797-y57:3(1093-1114)Online publication date: 1-Mar-2018
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