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

Liu, Bo, Zhang, Q and Gielen, G (2013) A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Black Box Optimization Problems. IEEE Transactions on Evolutionary Computation, 18 (2). pp. 180-192. ISSN 1089-778X

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Surrogate model assisted evolutionary algorithms (SAEAs) have recently attracted much attention due to the growing need for computationally expensive optimization in many real-world applications. Most current SAEAs, however, focus on small-scale problems. SAEAs for medium-scale problems (i.e., 20-50 decision variables) have not yet been well studied. In this paper, a Gaussian process surrogate model assisted evolutionary algorithm for medium-scale computationally expensive optimization problems (GPEME) is proposed and investigated. Its major components are a surrogate model-aware search mechanism for expensive optimization problems when a high-quality surrogate model is difficult to build and dimension reduction techniques for tackling the “curse of dimensionality.” A new framework is developed and used in GPEME, which carefully coordinates the surrogate modeling and the evolutionary search, so that the search can focus on a small promising area and is supported by the constructed surrogate model. Sammon mapping is introduced to transform the decision variables from tens of dimensions to a few dimensions, in order to take advantage of Gaussian process surrogate modeling in a low-dimensional space. Empirical studies on benchmark problems with 20, 30, and 50 variables and a real-world power amplifier design automation problem with 17 variables show the high efficiency and effectiveness of GPEME. Compared to three state-of-the-art SAEAs, better or similar solutions can be obtained with 12% to 50% exact function evaluations.

Item Type: Article
Keywords: Dimension reduction, Gaussian process, expensive optimization, prescreening, space mapping, surrogate model assisted evolutionary computation, surrogate models
Divisions: Applied Science, Computing and Engineering
Depositing User: Mr Stewart Milne
Date Deposited: 05 Aug 2015 14:25
Last Modified: 19 Dec 2017 15:30
URI: https://glyndwr.repository.guildhe.ac.uk/id/eprint/8322

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