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

Liu, Bo; Zhang, Qingfu; Gielen, Georges

doi:10.1109/tevc.2013.2248012

Cited by 464 publications

(222 citation statements)

References 21 publications

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“…It inherits the high search ability of EA to a large extent, but avoids the more necessary function evaluations of SAEAs with a standard EA structure. Comparisons show a substantial speed improvement compared to several state-of-the-art SAEAs using 20-30-dimensional continuous optimization problems [11]. However, pilot experiments show that SMAS can be trapped in local optima for EDOD.…”

Section: Introductionmentioning

confidence: 94%

See 1 more Smart Citation

A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

Liu

Sun

Zhang

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Real-world computationally expensive design optimization problems with discrete variables pose challenges to surrogate-based optimization methods in terms of both efficiency and search ability. In this paper, a new method is introduced, called surrogate model-aware differential evolution with neighbourhood exploration, which has two phases. The first phase adopts a surrogate-based optimization method based on efficient surrogate model-aware search framework, the goal of which is to reach at least the neighbourhood of the global optimum. In the second phase, a neighbourhood exploration method for discrete variables is developed and collaborates with the first phase to further improve the obtained solutions. Empirical studies on various benchmark problems and a realworld network-on-chip design optimization problem show the combined advantages in terms of efficiency and search ability: when only a very limited number of exact evaluations are allowed, the proposed method is not slower than one of the most efficient methods for the targeted problem; when more evaluations are allowed, the proposed method can obtain results with comparable quality compared to standard differential evolution, but it requires only 1% to 30% of exact function evaluations.

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Section: Introductionmentioning

confidence: 94%

“…The surrogate model-aware evolutionary search (SMAS) [11] framework uses a population-based search but with a new algorithm structure different from standard EA. It inherits the high search ability of EA to a large extent, but avoids the more necessary function evaluations of SAEAs with a standard EA structure.…”

Section: Introductionmentioning

confidence: 99%

A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

Liu

Sun

Zhang

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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show abstract

“…Currently, the common fitness estimation methods include the fitness inheritance and the application of surrogate model [11][12][13][14][15][16][17]. However, which method will perform better in fitness estimation?…”

Section: Introductionmentioning

confidence: 99%

Efficient Query for Historical Data in Evolutionary Algorithm

Tian¹,

Pan²,

Huang³

2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

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Abstract-For the time-consuming problem in calculating the fitness value, this paper proposes a hash bucket with precision mechanism for a quick query of the data in the neighborhood of a particle. In order to establish a balance between the calculation accuracy and utility, it uses the hash tables with precision mechanism to solve the problem in the storage and query of historical calculation data so that the neighborhood of a to-be-evaluated individual can be determined more accurately to reduce the error in estimating the fitness value. Moreover, it uses the typical reference functions to separately test the effectiveness and accuracy of the algorithms based on the values obtained in different dimensions. The test result proves that compared with the other algorithms described in this paper, our algorithm can provide a better solution in the context of the same number of times for fitness calculation.

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“…[6][7][8][9] However, these works typically assume that the function f (x) can be evaluated for any vector x with admissible component values. In contrast, the present work concerns problems of chemometrics interest in which the possible choices for x are restricted to a finite pool of objects available for selection.…”

Section: Introductionmentioning

confidence: 99%

An Active Search Method for Finding Objects with Near-Optimal Property Values within a Given Set

Matta¹,

Paiva²,

Galvão³

et al. 2016

Journal of the Brazilian Chemical Society

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This paper proposes an active search method aimed at finding objects with optimal or nearoptimal y-property values, on the basis of x-variables obtained by indirect, less costly methods. The proposed method progresses in a sequential manner, starting from a small subset of objects with known y-values. At each iteration, the K-nearest neighbour regression technique is employed to obtain estimates ŷ for the objects with unknown y-values. The object with best ŷ value is then subjected to a direct analysis procedure for evaluation of the y-property. Examples are presented with simulated data, as well as actual quantitative structure-activity relationship (QSAR) and near-infrared (NIR) spectrometry datasets. The QSAR and NIR case studies involve the search for maximal antidepressant activity in a set of arylpiperazine compounds and maximal pulp yield in a set of eucalyptus wood samples, respectively. In all these cases, the active search yielded results closer to the maximal y-value compared to the classical Kennard-Stone algorithm for object selection.

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

Cited by 464 publications

References 21 publications

A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

Efficient Query for Historical Data in Evolutionary Algorithm

An Active Search Method for Finding Objects with Near-Optimal Property Values within a Given Set

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