Gaussian Process Surrogate Models for the CMA Evolution Strategy

2019 ◽  
Vol 27 (4) ◽  
pp. 665-697 ◽  
Author(s):  
Lukáš Bajer ◽  
Zbyněk Pitra ◽  
Jakub Repický ◽  
Martin Holeňa
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
State Of The Art ◽  
Surrogate Models ◽  
Evolutionary Strategy ◽  
Uncertainty Prediction ◽  
Process Uncertainty ◽  
Covariance Matrix Adaptation ◽  
Model Predictions ◽  
Selection Of

This article deals with Gaussian process surrogate models for the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES)—several already existing and two by the authors recently proposed models are presented. The work discusses different variants of surrogate model exploitation and focuses on the benefits of employing the Gaussian process uncertainty prediction, especially during the selection of points for the evaluation with a surrogate model. The experimental part of the article thoroughly compares and evaluates the five presented Gaussian process surrogate and six other state-of-the-art optimizers on the COCO benchmarks. The algorithm presented in most detail, DTS-CMA-ES, which combines cheap surrogate-model predictions with the objective function evaluations in every iteration, is shown to approach the function optimum at least comparably fast and often faster than the state-of-the-art black-box optimizers for budgets of roughly 25–100 function evaluations per dimension, in 10- and less-dimensional spaces even for 25–250 evaluations per dimension.

scholarly journals Selection of Existing Sail Designs for Multi-Fidelity Surrogate Models

2022 ◽  
Vol 7 (01) ◽  
pp. 31-51
Author(s):  
Tanya Peart ◽  
Nicolas Aubin ◽  
Stefano Nava ◽  
John Cater ◽  
Stuart Norris
Keyword(s):  
Surrogate Model ◽  
Lift Coefficient ◽  
Simulation Method ◽  
Surrogate Models ◽  
High Fidelity ◽  
Data Points ◽  
Velocity Prediction ◽  
The Difference ◽  
Fidelity Model ◽  
Selection Of

Velocity Prediction Programs (VPPs) are commonly used to help predict and compare the performance of different sail designs. A VPP requires an aerodynamic input force matrix which can be computationally expensive to calculate, limiting its application in industrial sail design projects. The use of multi-fidelity kriging surrogate models has previously been presented by the authors to reduce this cost, with high-fidelity data for a new sail being modelled and the low-fidelity data provided by data from existing, but different, sail designs. The difference in fidelity is not due to the simulation method used to obtain the data, but instead how similar the sail’s geometry is to the new sail design. An important consideration for the construction of these models is the choice of low-fidelity data points, which provide information about the trend of the model curve between the high-fidelity data. A method is required to select the best existing sail design to use for the low-fidelity data when constructing a multi-fidelity model. The suitability of an existing sail design as a low fidelity model could be evaluated based on the similarity of its geometric parameters with the new sail. It is shown here that for upwind jib sails, the similarity of the broadseam between the two sails best indicates the ability of a design to be used as low-fidelity data for a lift coefficient surrogate model. The lift coefficient surrogate model error predicted by the regression is shown to be close to 1% of the lift coefficient surrogate error for most points. Larger discrepancies are observed for a drag coefficient surrogate error regression.

Interpreting testing and assessment: A state-of-the-art review

2021 ◽  
pp. 026553222110361
Author(s):  
Chao Han
Keyword(s):  
State Of The Art ◽  
Professional Certification ◽  
Automatic Assessment ◽  
Prospective Students ◽  
Assessment Practice ◽  
High Stakes ◽  
Future Directions ◽  
Assessment Tasks ◽  
Interpreter Education ◽  
Selection Of

Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the selection of prospective students, the certification of interpreters, and the confirmation/refutation of research hypotheses. However, few reviews exist providing a comprehensive mapping of relevant practice and research. The present article therefore aims to offer a state-of-the-art review, summarizing the existing literature and discovering potential lacunae. In particular, the article first provides an overview of interpreting ability/competence and relevant research, followed by main testing and assessment practice (e.g., assessment tasks, assessment criteria, scoring methods, specificities of scoring operationalization), with a focus on operational diversity and psychometric properties. Second, the review describes a limited yet steadily growing body of empirical research that examines rater-mediated interpreting assessment, and casts light on automatic assessment as an emerging research topic. Third, the review discusses epistemological, psychometric, and practical challenges facing interpreting testers. Finally, it identifies future directions that could address the challenges arising from fast-changing pedagogical, educational, and professional landscapes.

scholarly journals Estimating a Stoichiometric Solid’s Gibbs Free Energy Model by Means of a Constrained Evolutionary Strategy

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 471
Author(s):  
Constantino Grau Turuelo ◽  
Sebastian Pinnau ◽  
Cornelia Breitkopf
Keyword(s):  
Heat Capacity ◽  
Free Energy ◽  
Evolutionary Strategy ◽  
Data Sets ◽  
Automatic Data ◽  
Physical Constraints ◽  
Stoichiometric Model ◽  
Covariance Matrix Adaptation ◽  
Dsc Measurements ◽  
Capacity Data

Modeling of thermodynamic properties, like heat capacities for stoichiometric solids, includes the treatment of different sources of data which may be inconsistent and diverse. In this work, an approach based on the covariance matrix adaptation evolution strategy (CMA-ES) is proposed and described as an alternative method for data treatment and fitting with the support of data source dependent weight factors and physical constraints. This is applied to a Gibb’s Free Energy stoichiometric model for different magnesium sulfate hydrates by means of the NASA9 polynomial. Its behavior is proved by: (i) The comparison of the model to other standard methods for different heat capacity data, yielding a more plausible curve at high temperature ranges; (ii) the comparison of the fitted heat capacity values of MgSO4·7H2O against DSC measurements, resulting in a mean relative error of a 0.7% and a normalized root mean square deviation of 1.1%; and (iii) comparing the Van’t Hoff and proposed Stoichiometric model vapor-solid equilibrium curves to different literature data for MgSO4·7H2O, MgSO4·6H2O, and MgSO4·1H2O, resulting in similar equilibrium values, especially for MgSO4·7H2O and MgSO4·6H2O. The results show good agreement with the employed data and confirm this method as a viable alternative for fitting complex physically constrained data sets, while being a potential approach for automatic data fitting of substance data.

Selection of M-dwarfs using Gaia, WISE, and 2MASS

2019 ◽  
Vol 490 (3) ◽  
pp. 4107-4120
Author(s):  
J Bentley ◽  
C G Tinney ◽  
S Sharma ◽  
D Wright
Keyword(s):  
Absolute Magnitude ◽  
Contamination Rate ◽  
M Dwarfs ◽  
Sky Survey ◽  
Model Predictions ◽  
Galaxy Model ◽  
M Dwarf ◽  
Different Sources ◽  
Sources Of Contamination ◽  
Selection Of

ABSTRACT We present criteria for the selection of M-dwarfs down to G < 14.5 using all-sky survey data, with a view to identifying potential M-dwarfs, to be confirmed spectroscopically by the FunnelWeb survey. Two sets of criteria were developed. The first, based on absolute magnitude in the Gaia G passband, with MG > 7.7, selects 76,392 stars, with 81.0 per cent expected to be M-dwarfs at a completeness of >97 per cent. The second is based on colour and uses Gaia, WISE, and 2MASS all-sky photometry. This criteria identifies 94,479 candidate M-dwarfs, of which between 29.4 per cent and 47.3 per cent are expected to be true M-dwarfs, and which contains 99.6 per cent of expected M-dwarfs. Both criteria were developed using synthetic galaxy model predictions, and a previously spectroscopically classified set of M- and K-dwarfs, to evaluate both M-dwarf completeness and false-positive detections (i.e. the non-M-dwarf contamination rate). Both criteria used in combination demonstrate how each excludes different sources of contamination. We therefore developed a final set of criteria that combines absolute magnitude and colour selection to identify 74,091 stars. All these sets of criteria select numbers of objects feasible for confirmation via massively multiplexed spectroscopic surveys like FunnelWeb.

Efficient Multi-Objective Optimization of Labyrinth Seal Leakage in Steam Turbines Based on Hybrid Surrogate Models

2016 ◽  
Author(s):  
Kevin Cremanns ◽  
Dirk Roos ◽  
Simon Hecker ◽  
Peter Dumstorff ◽  
Henning Almstedt ◽  
...  
Keyword(s):  
Steam Turbine ◽  
Surrogate Model ◽  
Power Plants ◽  
Polynomial Regression ◽  
Renewable Energy Sources ◽  
Surrogate Models ◽  
Future Market ◽  
Steam Turbines ◽  
Model Calculations ◽  
Highly Efficient

The demand for energy is increasingly covered through renewable energy sources. As a consequence, conventional power plants need to respond to power fluctuations in the grid much more frequently than in the past. Additionally, steam turbine components are expected to deal with high loads due to this new kind of energy management. Changes in steam temperature caused by rapid load changes or fast starts lead to high levels of thermal stress in the turbine components. Therefore, todays energy market requires highly efficient power plants which can be operated under flexible conditions. In order to meet the current and future market requirements, turbine components are optimized with respect to multi-dimensional target functions. The development of steam turbine components is a complex process involving different engineering disciplines and time-consuming calculations. Currently, optimization is used most frequently for subtasks within the individual discipline. For a holistic approach, highly efficient calculation methods, which are able to deal with high dimensional and multidisciplinary systems, are needed. One approach to solve this problem is the usage of surrogate models using mathematical methods e.g. polynomial regression or the more sophisticated Kriging. With proper training, these methods can deliver results which are nearly as accurate as the full model calculations themselves in a fraction of time. Surrogate models have to face different requirements: the underlying outputs can be, for example, highly non-linear, noisy or discontinuous. In addition, the surrogate models need to be constructed out of a large number of variables, where often only a few parameters are important. In order to achieve good prognosis quality only the most important parameters should be used to create the surrogate models. Unimportant parameters do not improve the prognosis quality but generate additional noise to the approximation result. Another challenge is to achieve good results with as little design information as possible. This is important because in practice the necessary information is usually only obtained by very time-consuming simulations. This paper presents an efficient optimization procedure using a self-developed hybrid surrogate model consisting of moving least squares and anisotropic Kriging. With its maximized prognosis quality, it is capable of handling the challenges mentioned above. This enables time-efficient optimization. Additionally, a preceding sensitivity analysis identifies the most important parameters regarding the objectives. This leads to a fast convergence of the optimization and a more accurate surrogate model. An example of this method is shown for the optimization of a labyrinth shaft seal used in steam turbines. Within the optimization the opposed objectives of minimizing leakage mass flow and decreasing total enthalpy increase due to friction are considered.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

scholarly journals A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saba Moeinizade ◽  
Ye Han ◽  
Hieu Pham ◽  
Guiping Hu ◽  
Lizhi Wang
Keyword(s):  
Monte Carlo ◽  
State Of The Art ◽  
Simulation Method ◽  
Multiple Trait ◽  
Look Ahead ◽  
Probability Of Success ◽  
Genetic Distribution ◽  
Selection Of ◽  
Trait Introgression

AbstractMultiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. Our objective is to select the most promising individuals for further backcrossing or selfing. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches. Our proposed selection method can assist breeders to efficiently design trait introgression projects.

scholarly journals A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Benchmark Problems ◽  
Yield Estimation ◽  
Electromagnetic Devices ◽  
Main Challenge ◽  
Sample Points ◽  
Stochastic Yield

Abstract In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian process regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches.

Surrogate Population Models for Large-Scale Neural Simulations

2015 ◽  
Vol 27 (6) ◽  
pp. 1186-1222 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Surrogate Model ◽  
Latent Variables ◽  
Large Scale ◽  
Neural Model ◽  
Surrogate Models ◽  
Weighted Sums ◽  
Neural Systems ◽  
Neuron Models ◽  
Linear Filters ◽  
Population Activity

Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to multiscale modeling of neural systems that involves constructing efficient surrogate models of populations. Given a population of neuron models with correlated activity and with specific, nonrandom connections, a surrogate model is constructed in order to approximate the aggregate outputs of the population. The surrogate model requires less computation than the neural model, but it has a clear and specific relationship with the neural model. For example, approximate spike rasters for specific neurons can be derived from a simulation of the surrogate model. This article deals specifically with neural engineering framework (NEF) circuits of leaky-integrate-and-fire point neurons. Weighted sums of spikes are modeled by interpolating over latent variables in the population activity, and linear filters operate on gaussian random variables to approximate spike-related fluctuations. It is found that the surrogate models can often closely approximate network behavior with orders-of-magnitude reduction in computational demands, although there are certain systematic differences between the spiking and surrogate models. Since individual spikes are not modeled, some simulations can be performed with much longer steps sizes (e.g., 20 ms). Possible extensions to non-NEF networks and to more complex neuron models are discussed.

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