Статті в журналах з теми "Surrogate Function"
Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Surrogate Function.
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Surrogate Function".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
THIEL, MARCO, M. CARMEN ROMANO, UDO SCHWARZ, JÜRGEN KURTHS, and JENS TIMMER. "SURROGATE-BASED HYPOTHESIS TEST WITHOUT SURROGATES." International Journal of Bifurcation and Chaos 14, no. 06 (June 2004): 2107–14. http://dx.doi.org/10.1142/s0218127404010527.
Повний текст джерелаАнотація:
Fourier surrogate data are artificially generated time series, that — based on a resampling scheme — share the linear properties with an observed time series. In this paper we study a statistical surrogate hypothesis test to detect deviations from a linear Gaussian process with respect to asymmetry in time (Q-statistic). We apply this test to a Fourier representable function and obtain a representation of the asymmetry in time of the sample data, a characteristic for nonlinear processes, and the significance in terms of the Fourier coefficients. The main outcome is that we calculate the expected value of the mean and the standard deviation of the asymmetries of the surrogate data analytically and hence, no surrogates have to be generated. To illustrate the results we apply our method to the saw tooth function, the Lorenz system and to measured X-ray data of Cygnus X-1.
2
Ziff, Elizabeth. "“Honey, I Want to Be a Surrogate”: How Military Spouses Negotiate and Navigate Surrogacy With Their Service Member Husbands." Journal of Family Issues 40, no. 18 (July 18, 2019): 2774–800. http://dx.doi.org/10.1177/0192513x19862843.
Повний текст джерелаАнотація:
This article examines how military spouses negotiate the decision to become a surrogate with their service member husband and how the two navigate surrogacy together. It is speculated that military spouses are ideal candidates for surrogacy due to their particular status as a military spouse; however, military spouses face structural constraints in their everyday lives which in turn would prove challenging to their desire to become a surrogate. Based on in-depth interviews with 33 military spouses who had been surrogates, this article examines how military spouses discuss, negotiate, and experience surrogacy with their spouses all the while navigating the structural demands of the military and the contractual demands of surrogacy. Findings highlight egalitarian decision making between the spouses, and a mostly collaborative approach to the surrogacy process. Ultimately, this work illuminates how surrogacy is experienced by the women who participate in the practice and provides insight as to how military marriages function.
3
Ambarwati, Mega Dewi, and Ghina Azmita Kamila. "THE EVALUATION OF SURROGACY’S LEGAL SYSTEM IN INDONESIA AS COMPARISON TO INDIA’S LEGISLATION." Diponegoro Law Review 4, no. 2 (October 1, 2019): 167. http://dx.doi.org/10.14710/dilrev.4.2.2019.167-180.
Повний текст джерелаАнотація:
Nowadays, in marriage life, spouse often dealing with big problem as like infertility which make them unable to have offspring. However, due to infertility, the spouse has obtained some efforts to solve their problems. One way to solve the problem is by obtaining surrogacy with the help of surrogate mother. Nevertheless, in Indonesia, especially, surrogacy as well as surrogate mother is still considered to be taboo things and no legal system which regulate the surrogacy and/or surrogate mother. Yet other countries have allowed or legalize the surrogacy practice as well as surrogate mother. Hence, this study aimed to reveal a comparison of legal system on surrogate mother and surrogacy law in Indonesia and India. This study used comparative legal research methodology through the functional method since Indonesia has the same function over the purpose of law establishment on the surrogate mother in India. The result reveals that it needs a legal system on surrogacy and surrogate mother as the legal certainty for any individual especially spouse who could not have offspring along with some reasons such as minimalize prostitution and unregistered marriage, prevent dispute, and to develop scientific field.
4
Tenne, Yoel. "An Analysis of the RBF Hyperparameter Impact on Surrogate-Assisted Evolutionary Optimization." Scientific Programming 2022 (December 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/5175941.
Повний текст джерелаАнотація:
Computationally expensive optimization problems are often solved using surrogates and a common variant is the radial basis functions (RBF) model. It aggregates several basis functions which all depend on a hyperparameter affecting their individual outputs and consequentially the overall surrogate prediction. However, the optimal value of the hyperparameter is typically unknown and should therefore be calibrated. This raises the question how does the hyperparameter affect the overall optimization search effectiveness (and not just the stand-alone surrogate accuracy) and to what extent is such a calibration beneficial, which is an important consideration both for end-users and algorithm researchers alike. To rigorously address this issue this paper presents an analysis based on an extensive set of numerical experiments with an RBF surrogate-assisted evolutionary algorithm. It follows that the hyperparameter strongly affected performance and that the extent of its impact varied depending on the basis function, objective function modality, and problem dimension. Overall, calibration of the hyperparameter was typically highly beneficial to the search performance while dynamically optimizing the hyperparameter during the search yielded additional performance gains.
5
Liu, Bolin, and Liyang Xie. "Reliability Analysis of Structures by Iterative Improved Ensemble of Surrogate Method." Shock and Vibration 2019 (October 24, 2019): 1–13. http://dx.doi.org/10.1155/2019/6357104.
Повний текст джерелаАнотація:
Surrogate models have been widely adopted for reliability analysis. The common approach is to construct a series of surrogates based on a training set and then pick out the best one with the highest accuracy as an approximation of the time-consuming limit state function. However, the traditional method increases the risk of adopting an inappropriate model and does not take full advantage of the data devoted to constructing different surrogates. Furthermore, obtaining more samples is very expensive and sometimes even impossible. Therefore, to save the cost of constructing the surrogate and improve the prediction accuracy, an ensemble strategy is proposed in this paper for efficiently analyzing the structural reliability. The values of the weights are obtained by a recursive process and the leave-one-out technique, in which the values are updated in each iteration until a given prediction accuracy is achieved. Besides, a learning function is used to guide the selection of the next sampling candidate. Because the learning function utilizes the uncertainty estimator of the surrogate to guide the design of experiments (DoE), to accurately calculate the uncertainty estimator of the ensemble of surrogates, the concept of weighted mean square error is proposed. After the high-quality ensemble of surrogates of the limit state function is available, the Monte Carlo method is employed to calculate the failure probabilities. The proposed method is evaluated by three analytic problems and one engineering problem. The results show that the proposed ensemble of surrogates has better prediction accuracy and robustness than the stand-alone surrogates and the existing ensemble techniques.
6
Zeng, Wei, Xian Chao Wang, and Ying Sheng Wang. "Surrogating for High Dimensional Computationally Expensive Multi-Modal Functions with Elliptical Basis Function Models." Applied Mechanics and Materials 733 (February 2015): 880–84. http://dx.doi.org/10.4028/www.scientific.net/amm.733.880.
Повний текст джерелаАнотація:
In the engineering design process, approximation Technique could guarantee the fitting precision, speed up the design process and reduce design costs. To a certain extent, surrogate models could replace time-consuming and highly accurate computational fluid dynamics analysis gradually. In this paper, we take Optimal Latin Hypercube Sampling experimental design strategies to determine the sample space and error analysis test sample, adopt the principle of infilling criteria based on the maximum error to improve the accuracy of the surrogate model, test the unimodal and multimodal expensive functions of 10 dimension, 20 dimensions and 30 dimensions, study the performance and scope of EBF-NN surrogate model based on infilling criteria by comparing the RBF-NN surrogate model.
7
Iuliano, Emiliano. "Efficient Design Optimization Assisted by Sequential Surrogate Models." International Journal of Aerospace Engineering 2019 (May 12, 2019): 1–34. http://dx.doi.org/10.1155/2019/4937261.
Повний текст джерелаАнотація:
The paper proposes a global optimization algorithm employing surrogate modeling and adaptive infill criteria. The surrogates are exploited to screen the design space and provide lower-fidelity predictions across it; on the other hand, specific criteria are designed to suggest new points for high-fidelity evaluation so as to enrich the optimizer database. Both Kriging and radial basis function network are used as surrogates with different training strategies. Sequential design is achieved by introducing several infill criteria according to the realization of the exploration-exploitation trade-off. Optimization results are provided both for scalable and analytical test functions and for a practical aerodynamic shape optimization problem.
8
Malmquist, Anna, and Sonja Höjerström. "Constructions of surrogates, egg donors, and mothers: Swedish gay fathers’ narratives." Feminism & Psychology 30, no. 4 (May 14, 2020): 508–28. http://dx.doi.org/10.1177/0959353520922415.
Повний текст джерелаАнотація:
The study explored in detail how Swedish gay fathers (through surrogacy) talked about the surrogate mother and the egg donor. Thirteen semi-structured interviews with 22 gay fathers were conducted and analysed using critical discursive analysis. The surrogates were primarily constructed as a close family member, but occasionally in terms of their instrumental function. They were often described as active and independent, but occasionally as vulnerable or exploited. The egg donors were in some interviews constructed as close family members, while others talked about them as distant acquaintances. Further, donors were constructed either as a significant individual (for the fathers), or as an instrumental provider of the oocyte. While some participants constructed the surrogate and/or donor as their child’s mother(s), others were more reluctant or ambivalent about the mother construct. In conclusion, the participants engaged in rhetorical work that shed a positive light on surrogacy, and their own decisions were depicted as solid, ethical and genuine. The participants’ positive framing can be understood as the production of a counter discourse, in relation to an ongoing debate in Sweden, in which surrogacy is constructed as exploitation, dehumanization and prostitution.
9
&NA;. "Is endothelial function a useful surrogate?" Inpharma Weekly &NA;, no. 1256 (September 2000): 2. http://dx.doi.org/10.2165/00128413-200012560-00002.
Повний текст джерела
10
Chodos, Alan, and Eric Myers. "Testing the surrogate zeta-function method." Canadian Journal of Physics 64, no. 5 (May 1, 1986): 633–36. http://dx.doi.org/10.1139/p86-117.
Повний текст джерелаАнотація:
Use of the surrogate zeta-function method was crucial in calculating the Casimir energy in non-Abelian Kaluza–Klein theories. We establish the validity of this method for the case where the background metric is (Euclidean space) × (N sphere). Our techniques do not apply to the case where the background is (Minkowski space) × (N sphere).
11
Bemporad, Alberto. "Global optimization via inverse distance weighting and radial basis functions." Computational Optimization and Applications 77, no. 2 (July 27, 2020): 571–95. http://dx.doi.org/10.1007/s10589-020-00215-w.
Повний текст джерелаАнотація:
Abstract Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples. The acquisition step trades off between seeking for a new optimization vector where the surrogate is minimum (exploitation of the surrogate) and looking for regions of the feasible space that have not yet been visited and that may potentially contain better values of the objective function (exploration of the feasible space). This paper proposes a new global optimization algorithm that uses inverse distance weighting (IDW) and radial basis functions (RBF) to construct the acquisition function. Rather arbitrary constraints that are simple to evaluate can be easily taken into account. Compared to Bayesian optimization, the proposed algorithm, that we call GLIS (GLobal minimum using Inverse distance weighting and Surrogate radial basis functions), is competitive and computationally lighter, as we show in a set of benchmark global optimization and hyperparameter tuning problems. MATLAB and Python implementations of GLIS are available at http://cse.lab.imtlucca.it/~bemporad/glis.
12
Cunningham, Thomas V., Leslie P. Scheunemann, Robert M. Arnold, and Douglas White. "How do clinicians prepare family members for the role of surrogate decision-maker?" Journal of Medical Ethics 44, no. 1 (July 17, 2017): 21–26. http://dx.doi.org/10.1136/medethics-2016-103808.
Повний текст джерелаАнотація:
PurposeAlthough surrogate decision-making (SDM) is prevalent in intensive care units (ICUs) and concerns with decision quality are well documented, little is known about how clinicians help family members understand the surrogate role. We investigated whether and how clinicians provide normative guidance to families regarding how to function as a surrogate.Subjects and methodsWe audiorecorded and transcribed 73 ICU family conferences in which clinicians anticipated discussing goals of care for incapacitated patients at high risk of death. We developed and applied a coding framework to identify normative statements by clinicians regarding what considerations should guide surrogates’ decisions, including whether clinicians explained one or more of Buchanan and Brock’s three standard principles of SDM to family members.ResultsClinicians made at least one statement about how to perform the surrogate role in 24 (34%) conferences (mean of 0.83 statements per conference (1.77; range 0–9)). We observed three general types of normative guidance provided to surrogates, with some conferences containing more than one type of guidance: counselling about one or more standard principles of SDM (24% of conferences); counselling surrogates to make decisions centred on the patient as a person, without specifying how to accomplish that (14% of conferences); and counselling surrogates to make decisions based on the family’s values (8% of conferences).ConclusionsClinicians did not provide normative guidance about the surrogate role in two-thirds of family conferences for incapacitated patients at high risk for death. When they did, clinicians’ guidance was often incomplete and sometimes conflicted with standard principles of SDM. Future work is needed to understand whether providing explicit guidance on how to perform the surrogate role improves decision-making or mitigates surrogates’ psychological distress.
13
Pan, Jeng-Shyang, Li-Gang Zhang, Shu-Chuan Chu, Chin-Shiuh Shieh, and Junzo Watada. "Surrogate-Assisted Hybrid Meta-Heuristic Algorithm with an Add-Point Strategy for a Wireless Sensor Network." Entropy 25, no. 2 (February 9, 2023): 317. http://dx.doi.org/10.3390/e25020317.
Повний текст джерелаАнотація:
Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems.
14
Younis, Adel, and Zuomin Dong. "High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm." Algorithms 15, no. 8 (August 7, 2022): 279. http://dx.doi.org/10.3390/a15080279.
Повний текст джерелаАнотація:
The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As a result, surrogate models that require far less time and resources to analyze are used in place of these time-consuming analyses. In multi-objective optimization (MOO) problems involving pricey analysis and simulation techniques such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions. In order to reduce the expense of fitness function evaluations and locate the Pareto frontier for MOO problems, the automated multiobjective surrogate based Pareto finder MOO algorithm (AMSP) is proposed. Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models. The algorithm repeats the process of sampling and updating the Pareto set, by assigning weighting factors to those surrogates in accordance with the values of the root mean squared error, until a Pareto frontier is discovered. AMSP was successfully employed to identify the Pareto set and the Pareto border. Utilizing multi-objective benchmark test functions and engineering design examples such airfoil shape geometry of wind turbine, the unique approach was put to the test. The cost of computing the Pareto optima for test functions and real engineering design problem is reduced, and promising results were obtained.
15
Dushatskiy, Arkadiy, Tanja Alderliesten, and Peter A. N. Bosman. "A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies." ACM Transactions on Evolutionary Learning and Optimization 1, no. 2 (July 23, 2021): 1–23. http://dx.doi.org/10.1145/3453141.
Повний текст джерелаАнотація:
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.
16
Havinga, Jos, Gerrit Klaseboer, and A. H. van den Boogaard. "Sequential Optimization of Strip Bending Process Using Multiquadric Radial Basis Function Surrogate Models." Key Engineering Materials 554-557 (June 2013): 911–18. http://dx.doi.org/10.4028/www.scientific.net/kem.554-557.911.
Повний текст джерелаАнотація:
Surrogate models are used within the sequential optimization strategy for forming processes. A sequential improvement (SI) scheme is used to refine the surrogate model in the optimal region. One of the popular surrogate modeling methods for SI is Kriging. However, the global response of Kriging models deteriorates in some cases due to local model refinement within SI. This may be problematic for multimodal optimization problems and for other applications where correct prediction of the global response is needed. In this paper the deteriorating global behavior of the Kriging surrogate modeling technique is shown for a model of a strip bending process. It is shown that a Radial Basis Function (RBF) surrogate model with Multiquadric (MQ) basis functions performs equally well in terms of optimization efficiency and better in terms of global predictive accuracy. The local point density is taken into account in the model formulation.
17
Lu, Dan, and Daniel Ricciuto. "Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques." Geoscientific Model Development 12, no. 5 (May 6, 2019): 1791–807. http://dx.doi.org/10.5194/gmd-12-1791-2019.
Повний текст джерелаАнотація:
Abstract. Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine-learning-based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change, such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly reduces computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between eight model parameters and 42 660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42 660 variables, wherein the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency of data–model integration to improve predictions and advance our understanding of the Earth system.
18
Solomon, Michael R. "The Missing Link: Surrogate Consumers in the Marketing Chain." Journal of Marketing 50, no. 4 (October 1986): 208–18. http://dx.doi.org/10.1177/002224298605000406.
Повний текст джерелаАнотація:
Though consumers commonly are assumed to be actively involved in important purchase decisions, the author proposes that consumers in fact often relinquish control to external experts, or surrogates, in such situations. As a result, the purchase decision is often a joint process over which the end consumer does not necessarily retain primary control. The author conceptualizes the surrogate function as an interface between the flow of market channels and the sequence of stages involved in consumer decision making. The roles played by surrogates at these stages are explored and ramifications for marketing theory, consumer research, and managerial practice are discussed.
19
Ouyang, Qi, Xiao Qian Chen, and Wen Yao. "Comparison of the Function Regression Method and Data Classification Method for Limit State Function Approximation." Advanced Materials Research 774-776 (September 2013): 1738–44. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1738.
Повний текст джерелаАнотація:
To reduce the computational burden of the reliability analysis of complex engineering application, approximate method is always used to construct the surrogate model of the implicit limit state function. Since the limit state function is a classifier of the failure domain and safe domain, its approximation can be established by the function regression method and data classification method. In this paper, these two methods are tested to several limit state functions including linear function, highly nonlinear function, high dimensional function, series system and parallel system. Least squares support vector machines are used to construct the surrogate models. A detail comparison of function regression method and data classification method for limit state function approximation is given. The conclusions of this paper can give guidance for the engineers to choose an appropriate approximate method in the engineering applications.
20
Chen, Guodong, Kai Zhang, Liming Zhang, Xiaoming Xue, Dezhuang Ji, Chuanjin Yao, Jun Yao, and Yongfei Yang. "Global and Local Surrogate-Model-Assisted Differential Evolution for Waterflooding Production Optimization." SPE Journal 25, no. 01 (December 9, 2019): 105–18. http://dx.doi.org/10.2118/199357-pa.
Повний текст джерелаАнотація:
Summary Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical simulator. In this paper, a new optimization algorithm called global and local surrogate-model-assisted differential evolution (GLSADE) is introduced for waterflooding production-optimization problems. The proposed method consists of two parts: (1) a global surrogate-model-assisted differential-evolution (DE) part, in which DE is used to generate multiple offspring, and (2) a local surrogate-model-assisted DE part, in which DE is used to search for the optimum of the surrogate. The cooperation between global optimization and local search helps the production-optimization process become more efficient and more effective. Compared with the conventional one-shot surrogate-based approach, the developed method iteratively selects data points to enhance the accuracy of the promising area of the surrogate model, which can substantially improve the optimization process. To the best of our knowledge, the proposed method uses a state-of-the-art surrogate framework for production-optimization problems. The approach is tested on two 100-dimensional benchmark functions, a three-channel model, and the egg model. The results show that the proposed method can achieve higher net present value (NPV) and better convergence speed in comparison with the traditional evolutionary algorithm and other surrogate-assisted optimization methods for production-optimization problems.
21
Badhurshah, Rameez, and Abdus Samad. "Surrogate Assisted Design Optimization of an Air Turbine." International Journal of Rotating Machinery 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/563483.
Повний текст джерелаАнотація:
Surrogates are cheaper to evaluate and assist in designing systems with lesser time. On the other hand, the surrogates are problem dependent and they need evaluation for each problem to find a suitable surrogate. The Kriging variants such as ordinary, universal, and blind along with commonly used response surface approximation (RSA) model were used in the present problem, to optimize the performance of an air impulse turbine used for ocean wave energy harvesting by CFD analysis. A three-level full factorial design was employed to find sample points in the design space for two design variables. A Reynolds-averaged Navier Stokes solver was used to evaluate the objective function responses, and these responses along with the design variables were used to construct the Kriging variants and RSA functions. A hybrid genetic algorithm was used to find the optimal point in the design space. It was found that the best optimal design was produced by the universal Kriging while the blind Kriging produced the worst. The present approach is suggested for renewable energy application.
22
Berkemeier, Manuel, and Sebastian Peitz. "Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models." Mathematical and Computational Applications 26, no. 2 (April 15, 2021): 31. http://dx.doi.org/10.3390/mca26020031.
Повний текст джерелаАнотація:
We present a local trust region descent algorithm for unconstrained and convexly constrained multiobjective optimization problems. It is targeted at heterogeneous and expensive problems, i.e., problems that have at least one objective function that is computationally expensive. Convergence to a Pareto critical point is proven. The method is derivative-free in the sense that derivative information need not be available for the expensive objectives. Instead, a multiobjective trust region approach is used that works similarly to its well-known scalar counterparts and complements multiobjective line-search algorithms. Local surrogate models constructed from evaluation data of the true objective functions are employed to compute possible descent directions. In contrast to existing multiobjective trust region algorithms, these surrogates are not polynomial but carefully constructed radial basis function networks. This has the important advantage that the number of data points needed per iteration scales linearly with the decision space dimension. The local models qualify as fully linear and the corresponding general scalar framework is adapted for problems with multiple objectives.
23
Claywell, Brian C., Vu Dinh, Mathieu Fourment, Connor O. McCoy, and Frederick A. Matsen IV. "A Surrogate Function for One-Dimensional Phylogenetic Likelihoods." Molecular Biology and Evolution 35, no. 1 (September 26, 2017): 242–46. http://dx.doi.org/10.1093/molbev/msx253.
Повний текст джерела
24
Frick, Matthias, and Franz Weidinger. "Endothelial Function: A Surrogate Endpoint in Cardiovascular Studies?" Current Pharmaceutical Design 13, no. 17 (June 1, 2007): 1741–50. http://dx.doi.org/10.2174/138161207780831211.
Повний текст джерела
25
Keane, Andy J., and Ivan I. Voutchkov. "Robust design optimization using surrogate models." Journal of Computational Design and Engineering 7, no. 1 (February 1, 2020): 44–55. http://dx.doi.org/10.1093/jcde/qwaa005.
Повний текст джерелаАнотація:
Abstract The use of surrogate models (response surface models, curve fits) of various types (radial basis functions, Gaussian process models, neural networks, support vector machines, etc.) is now an accepted way for speeding up design search and optimization in many fields of engineering that require the use of expensive computer simulations, including problems with multiple goals and multiple domains. Surrogates are also widely used in dealing with uncertainty quantification of expensive black-box codes where there are strict limits on the number of function evaluations that can be afforded in estimating the statistical properties of derived performance quantities. Here, we tackle the problem of robust design optimization from the direction of Gaussian process models (Kriging). We contrast two previously studied models, co-Kriging and combined Kriging (sometimes called level 1 Kriging), and propose a new combined approach called combined co-Kriging that attempts to make best use of the key ideas present in these methods.
26
Yi, Jin, Yichi Shen, and Christine A. Shoemaker. "A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems." Structural and Multidisciplinary Optimization 62, no. 4 (May 17, 2020): 1787–807. http://dx.doi.org/10.1007/s00158-020-02575-7.
Повний текст джерелаАнотація:
Abstract This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynamic coordinate search algorithm using response surface) algorithm to search on the LF model and then employ a potential area detection procedure to identify the promising points from the LF model. The promising points serve as the initial start points when we further search for the optimal solution based on the HF model. The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates). The comparisons are conducted on a multi-fidelity optimization test suite containing 10 problems with 10 and 30 dimensions. Besides the benchmark functions, we also apply the proposed algorithm to a practical and computationally expensive capacity planning problem in manufacturing systems which involves discrete event simulations. The experimental results demonstrate that MRSO outperforms all the compared methods.
27
Monakov, A. A. "A Versatile Algorithm for Autofocusing SAR Images." Journal of the Russian Universities. Radioelectronics 24, no. 1 (February 26, 2021): 22–33. http://dx.doi.org/10.32603/1993-8985-2021-24-1-22-33.
Повний текст джерелаАнотація:
Introduction. Random deviations of the antenna phase centre of a synthetic aperture radar (SAR) are a source of phase errors for the received signal. These phase errors frequently cause blurring of the radar image. The image quality can be improved using various autofocus algorithms. Such algorithms estimate phase errors via optimization of an objective function, which defines the radar image quality. The image entropy and sharpness are well known examples of objective functions. The objective function extremum can be found by fast optimization methods, whose realization is a challenging computing task.Aim. To synthesize a versatile and computationally simple autofocusing algorithm allowing any objective function to used without changing its structure significantly.Materials and methods. An algorithm based on substituting the selected objective function with a simpler surrogate objective function, whose extremum can be found by a direct method, is proposed. This method has been referred as the MM optimization in scientific literature. It is proposed to use a quadratic function as a surrogate objective function.Results. The synthesized algorithm is straightforward, not requiring recursive methods for finding the optimal solution. These advantages determine the enhanced speed and stability of the proposed algorithm. Adjusting the algorithm for the selected objective function requires minimal software changes. Compared to the algorithm using a linear surrogate objective function, the proposed algorithm provides a 1.5 times decrease in the standard deviation of the phase error estimate, with an approximately 10 % decrease in the number of iterations.Conclusion. The proposed autofocusing algorithm can be used in synthetic aperture radars to compensate the arising phase errors. The algorithm is based on the MM-optimization of the quadratic surrogate objective functions for radar images. The computer simulation results confirm the efficiency of the proposed algorithm even in case of large phase errors.
28
Kunakote, Tawatchai, and Sujin Bureerat. "Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/695172.
Повний текст джерелаАнотація:
The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous shape and sizing design problems of structures taking into account of structural weight, lateral bucking, natural frequency, and stress. Structural analysis is carried out by using a finite element procedure. The optimum results obtained are compared and discussed. The performance assessment is based on the hypervolume indicator. The performance of the surrogate models for estimating design constraints is investigated. It has been found that, by using a quadratic function surrogate model, the optimiser searching performance is greatly improved.
29
Mai, Hau T., Jaewook Lee, Joowon Kang, H. Nguyen-Xuan, and Jaehong Lee. "An Improved Blind Kriging Surrogate Model for Design Optimization Problems." Mathematics 10, no. 16 (August 12, 2022): 2906. http://dx.doi.org/10.3390/math10162906.
Повний текст джерелаАнотація:
Surrogate modeling techniques are widely employed in solving constrained expensive black-box optimization problems. Therein, Kriging is among the most popular surrogates in which the trend function is considered as a constant mean. However, it also encounters several challenges related to capturing the overall trend with a relatively limited number of function evaluations as well as searching feasible points with complex or discontinuous feasible regions. To address this above issue, this paper presents an improved surrogate blind Kriging (IBK) and a combined infill strategy to find the optimal solution. According to enhancing the prediction accuracy of metamodels of objective and constraints, the high-order effects of regression function in the blind Kriging are identified by promising a variable selection technique. In addition, an infill strategy is developed based on the probability of feasibility, penalization, and constrained expected improvement for updating blind Kriging metamodels of the objective and constraints. At each iteration, two infill sample points are allocated at the positions to achieve improvement in optimality and feasibility. The IBK metamodels are updated by the newly-added infill sample points, which leads the proposed framework search to rapidly converge to the optimal solution. The performance and applicability of the proposed model are tested on several numerical benchmark problems via comparing with other metamodel-based constrained optimization methods. The obtained results indicate that IBK generally has a greater efficiency performance and outperforms the competitors in terms of a limited number of function evaluations. Finally, IBK is successfully applied to structural design optimization. The optimization results show that IBK is able to find the best feasible design with fewer evaluation functions compared with other studies, and this demonstrates the effectiveness and practicality of the proposed model for solving the constrained expensive black-box engineering design optimization problems.
30
Zenke, Friedemann, and Tim P. Vogels. "The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks." Neural Computation 33, no. 4 (2021): 899–925. http://dx.doi.org/10.1162/neco_a_01367.
Повний текст джерелаАнотація:
Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. Yet how network connectivity relates to function is poorly understood, and the functional capabilities of models of spiking networks are still rudimentary. The lack of both theoretical insight and practical algorithms to find the necessary connectivity poses a major impediment to both studying information processing in the brain and building efficient neuromorphic hardware systems. The training algorithms that solve this problem for artificial neural networks typically rely on gradient descent. But doing so in spiking networks has remained challenging due to the nondifferentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients affect learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative's scale can substantially affect learning performance. When we combine surrogate gradients with suitable activity regularization techniques, spiking networks perform robust information processing at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks.
31
Zeng, Wei, Yue Yang, Huan Xie, and Lin-jun Tong. "CF-Kriging surrogate model based on the combination forecasting method." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 18 (August 9, 2016): 3274–84. http://dx.doi.org/10.1177/0954406215610149.
Повний текст джерелаАнотація:
The spatial correlation function (SCF) is an important part of the Kriging surrogate model that describes the sample data structure, and the SCF affects the fitting accuracy of the Kriging surrogate model directly. In a Kriging surrogate model, a single SCF is typically selected to describe the sample data structure, which may cause sample information loss and fitting error. A new Kriging surrogate model for combination forecasting (CF-Kriging) was constructed by integrating of linear weighted approach based on the combination forecasting method, in which the differences in the sample information described by the diverse SCFs for the same sample data structure were considered. The integrity of the sample information of the CF-Kriging model was improved using single-SCF Kriging surrogate models as sub-models and considering the minimum mean absolute percentage error as the improved target for the fitting accuracy. The effectiveness of the CF-Kriging surrogate model was demonstrated using four test functions and two engineering problems, which indicated that the CF-Kriging surrogate model could effectively improve the fitting accuracy and the fitting stability of an ordinary Kriging surrogate model.
32
Bajer, Lukáš, Zbyněk Pitra, Jakub Repický, and Martin Holeňa. "Gaussian Process Surrogate Models for the CMA Evolution Strategy." Evolutionary Computation 27, no. 4 (December 2019): 665–97. http://dx.doi.org/10.1162/evco_a_00244.
Повний текст джерелаАнотація:
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.
33
Hengel, Richard L., and Joseph A. Kovacs. "Surrogate Markers of Immune Function in Human Immunodeficiency Virus–Infected Patients: What Are They Surrogates For?" Journal of Infectious Diseases 188, no. 12 (December 15, 2003): 1791–93. http://dx.doi.org/10.1086/379901.
Повний текст джерела
34
Chechile, Richard A. "Using Hazard and Surrogate Functions for Understanding Memory and Forgetting." AppliedMath 2, no. 4 (October 4, 2022): 518–46. http://dx.doi.org/10.3390/appliedmath2040031.
Повний текст джерелаАнотація:
The retention of human memory is a process that can be understood from a hazard-function perspective. Hazard is the conditional probability of a state change at time t given that the state change did not yet occur. After reviewing the underlying mathematical results of hazard functions in general, there is an analysis of the hazard properties associated with nine theories of memory that emerged from psychological science. Five theories predict strictly monotonically decreasing hazard whereas the other four theories predict a peaked-shaped hazard function that rises initially to a peak and then decreases for longer time periods. Thus, the behavior of hazard shortly after the initial encoding is the critical difference among the theories. Several theorems provide a basis to explore hazard for the initial time period after encoding in terms of a more practical surrogate function that is linked to the behavior of the hazard function. Evidence for a peak-shaped hazard function is provided and a case is made for one particular psychological theory of memory that posits that memory encoding produces two redundant representations that have different hazard properties. One memory representation has increasing hazard while the other representation has decreasing hazard.
35
Ozol, Cengiz. "The Surrogate Wage Function and Capital: Theory with Measurement." Canadian Journal of Economics 24, no. 1 (February 1991): 175. http://dx.doi.org/10.2307/135485.
Повний текст джерела
36
SUGAI, Tomotaka, Kohei SHINTANI, Atsuji ABE, and Yasushi YAMAMOTO. "Surrogate modeling of transfer function using feature extraction method." Proceedings of Design & Systems Conference 2019.29 (2019): 2110. http://dx.doi.org/10.1299/jsmedsd.2019.29.2110.
Повний текст джерела
37
Pacheco, Jorge E., Cristina H. Amon, and Susan Finger. "Bayesian Surrogates Applied to Conceptual Stages of the Engineering Design Process." Journal of Mechanical Design 125, no. 4 (December 1, 2003): 664–72. http://dx.doi.org/10.1115/1.1631580.
Повний текст джерелаАнотація:
During the conceptual design stages, designers often have incomplete knowledge about the interactions among design parameters. We are developing a methodology that will enable designers to create models with levels of detail and accuracy that correspond to the current state of the design process. Thus, designers can create a rough surrogate model when only a few data points are available and then refine the model as the design progresses and more information becomes available. These surrogates represent the system response when limited information is available and when few realizations of experiments or numerical simulations are possible. This paper presents a covariance-based approach for building multistage surrogates in the conceptual design stages when bounds for the response are not available a priori. We test the methodology using a one-dimensional analytical function and a heat transfer problem with an analytical solution, in order to obtain error measurements. We then illustrate the use of the methodology in a thermal design problem for wearable computers. The surrogate model enables the designer to understand the relationships among the design parameters.
38
Jenkins, William F., and Peter Gerstoft. "Acquisition functions in Bayesian optimization of ocean acoustic waveguides using Gaussian processes." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A267. http://dx.doi.org/10.1121/10.0011293.
Повний текст джерелаАнотація:
Geoacoustic model optimization and inversion are computationally expensive endeavors. In cases where a parameter grid search is prohibitively expensive, optimization produces an approximated solution through sampling techniques such as Markov chain Monte Carlo, simulated annealing, and genetic algorithms. More recent work proposes replacing such sampling with a Bayesian approach that uses a Gaussian process as a surrogate model of the objective function. The surrogate model represents the posterior on the objective function and is updated with each sample evaluation. As an alternative to sampling methods listed above, acquisition functions incorporate the uncertainty in the posterior to select the next point to be sampled and can greatly speed the optimization. In this study, four common acquisition functions are described encompassing approaches that use manual tuning (upper confidence bound), improvement criteria (probability of improvement and expected improvement), and information criteria (entropy search). Results are presented from an acoustic parameter search for a shallow water waveguide for each function. Expected improvement is found to be the preferred acquisition function for the simulations, converging on the optimal solution more quickly than the other acquisition functions.
39
Karidis, J. P., and S. R. Turns. "Efficient Optimization of Computationally Expensive Objective Functions." Journal of Mechanisms, Transmissions, and Automation in Design 108, no. 3 (September 1, 1986): 336–39. http://dx.doi.org/10.1115/1.3258736.
Повний текст джерелаАнотація:
An algorithm is presented for the efficient constrained or unconstrained minimization of computationally expensive objective functions. The method proceeds by creating and numerically optimizing a sequence of surrogate functions which are chosen to approximate the behavior of the unknown objective function in parameter-space. The Recursive Surrogate Optimization (RSO) technique is intended for design applications where the computational cost required to evaluate the objective function greatly exceeds both the cost of evaluating any domain constraints present and the cost associated with one iteration of a typical optimization routine. Efficient optimization is achieved by reducing the number of times that the objective function must be evaluated at the expense of additional complexity and computational cost associated with the optimization procedure itself. Comparisons of the RSO performance on eight widely used test problems to published performance data for other efficient techniques demonstrate the utility of the method.
40
Lu, Li, Yizhong Wu, Qi Zhang, and Ping Qiao. "A Transformation-Based Improved Kriging Method for the Black Box Problem in Reliability-Based Design Optimization." Mathematics 11, no. 1 (January 1, 2023): 218. http://dx.doi.org/10.3390/math11010218.
Повний текст джерелаАнотація:
In order to overcome the drawbacks of expensive function evaluation in the practical reliability-based design optimization (RBDO) problem, researchers have proposed the black box-based RBDO method. The algorithm flow of the commonly employed RBDO method for the black box problem consists of the outer construction loop of the surrogate model of the constraint function and the inner surrogate model-based solving loop. To improve the solving ability of the black box RBDO problem, this paper proposes a transformation-based improved kriging method to increase the effectiveness of the two loops identified above. For the outer loop, a sample distribution-based learning function is suggested to improve the construction efficiency of the surrogate model of the constraint function. For the inner loop, a paired incremental sample-based limit reliability boundary construction approach is suggested to transform the RBDO problem into an equivalent deterministic design optimization problem that can be efficiently solved by classical optimization algorithms. The test results of five cases demonstrate that the proposed method can accurately construct the surrogate model of the constraint function and efficiently solve the black box RBDO problem.
41
Eggert, R. J., and R. W. Mayne. "Probabilistic Optimal Design Using Successive Surrogate Probability Density Functions." Journal of Mechanical Design 115, no. 3 (September 1, 1993): 385–91. http://dx.doi.org/10.1115/1.2919203.
Повний текст джерелаАнотація:
Probabilistic optimization using the moment matching method and the simulation optimization method are discussed and compared to conventional deterministic optimization. A new approach based on successively approximating probability density functions, using recursive quadratic programming for the optimization process, is described. This approach incorporates the speed and robustness of analytical probability density functions and improves accuracy by considering simulation results. Theoretical considerations and an example problem illustrate the features of the approach. The paper closes with a discussion of an objective function formulation which includes the expected cost of design constraint failure.
42
Jenkins, William F., and Peter Gerstoft. "Applications of Bayesian optimization with a Gaussian process surrogate model in ocean acoustics." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A157. http://dx.doi.org/10.1121/10.0015876.
Повний текст джерелаАнотація:
In this study, we present a method that samples geoacoustic parameter space with a Bayesian approach that uses a Gaussian process as a surrogate model of the objective function. The objective function is defined as a Bartlett processor whose output measures the match between a received and replica pressure field on a vertical line array. Replica fields are obtained using a normal mode propagation model whose geoacoustic parameters are selected from the parameter search space. The surrogate model represents the posterior on the objective function and is updated with each model evaluation. Optimization is performed with sequential model evaluations, with an acquisition function guiding the next point in parameter space to be evaluated. Various use cases and parameterizations are discussed, including the effects of the choice of acquisition function and covariance function of the Gaussian process. Results indicate that Bayesian optimization using a Gaussian process surrogate model converges rapidly on an approximated optimal solution.
43
Othman, Norazila, and Masahiro Kanazaki. "Development of Digital Flight Motion Methodology Based on Aerodynamic Derivatives Approximation." Journal of Robotics and Mechatronics 28, no. 2 (April 19, 2016): 215–25. http://dx.doi.org/10.20965/jrm.2016.p0215.
Повний текст джерелаАнотація:
[abstFig src='/00280002/12.jpg' width=""300"" text='3D contour views of Cz [-0.4—1.05],Mach:0.6-1.4, Alpha:0°-30°' ]The accuracy of efficient flight simulation depends on the quality of the aerodynamic data used to simulate aircraft dynamic motion. The accuracy of such data prediction depends strongly on motion variables, aerodynamic derivatives, and the coefficients used when the complete global aerodynamic database is being building. A surrogate model applied as a prediction method based on several measured points (exact function) used to predict unknown points of interest helps reduce time taken by the experiment or computation. Latin hypercube sampling searches the solution space for aerodynamic data to optimize the experimental design, so the key objective is to develop an aircraft's efficient digital flight motion by solving equations of motion and predicting aerodynamic data using a surrogate model. To realize these goals, we use sample surrogate model data, acquired from empirical model USAF Stability and Control DATCOM. The database was built for two main variables, the angle of attack and the Mach number, along the longitudinal and lateral axes. Exact and predicted functions were compared by calculating the mean squared error (MSE). The digital flight was validated through mode motion analysis and a flight quality scale to prove flight mission capabilities. A comparison between results predicted by the surrogate model and the exact function showed that flight simulation analysis and prediction ability of the surrogate model are useful in future analyses.
44
Zhao, Mengjie, Kai Zhang, Guodong Chen, Xinggang Zhao, Jun Yao, Chuanjin Yao, Liming Zhang, and Yongfei Yang. "A Classification-Based Surrogate-Assisted Multiobjective Evolutionary Algorithm for Production Optimization under Geological Uncertainty." SPE Journal 25, no. 05 (June 18, 2020): 2450–69. http://dx.doi.org/10.2118/201229-pa.
Повний текст джерелаАнотація:
Summary Multiobjective optimization (MOO) is a popular procedure for waterflooding optimization under geological uncertainty that maximizes the expectation of net present value (NPV) over all possible uncertainty models and minimizes the variance simultaneously. However, the optimization process involves a large number of decision variables, and the problem is computationally expensive. Surrogate-assisted evolutionary algorithms (SAEAs), which have proved to be an effective way to solve expensive problems, design computationally inexpensive functions to approximate each objective function. On the basis of characterization, we have designed an efficient multiobjective evolutionary algorithm (MOEA) to effectively deal with computationally expensive simulation-based optimization problems. The uniqueness of this algorithm is that it incorporates a Pareto-rank-learning scheme with surrogate-assisted infill criterion. The first is to introduce a multiclass error-correcting output codes (ECOC) model that directly predicts the dominance relationship between candidate solutions and prescreens, and the second is to train a radial-basis function (RBF) network that predicts the objective functions of prescreened solutions to calculate the hypervolume (HV) improvement that maintains convergence and diversity. Compared with typical surrogate-based methods, the developed method provides a classifier first that can enhance the accuracy in high dimensions and reduce computational complexity. To the best of our knowledge, the proposed method compares with state-of-the-art surrogate frameworks for multiobjective production-optimization problems. In this paper, the proposed approach is applied to two 200D benchmark problems and two synthetic reservoir models. The results show that the proposed method can achieve more comprehensive and efficient reservoir management (RM) with a higher convergence speed compared with traditional MOEAs and surrogate-assisted optimization methods.
45
Shafie Khorassani, Fatema, Jeremy M. G. Taylor, Niko Kaciroti, and Michael R. Elliott. "Incorporating Covariates into Measures of Surrogate Paradox Risk." Stats 6, no. 1 (February 17, 2023): 322–44. http://dx.doi.org/10.3390/stats6010020.
Повний текст джерелаАнотація:
Clinical trials often collect intermediate or surrogate endpoints other than their true endpoint of interest. It is important that the treatment effect on the surrogate endpoint accurately predicts the treatment effect on the true endpoint. There are settings in which the proposed surrogate endpoint is positively correlated with the true endpoint, but the treatment has opposite effects on the surrogate and true endpoints, a phenomenon labeled “surrogate paradox”. Covariate information may be useful in predicting an individual’s risk of surrogate paradox. In this work, we propose methods for incorporating covariates into measures of assessing the risk of surrogate paradox using the meta-analytic causal association framework. The measures calculate the probability that a treatment will have opposite effects on the surrogate and true endpoints and determine the size of a positive treatment effect on the surrogate endpoint that would reduce the risk of a negative treatment effect on the true endpoint as a function of covariates, allowing the effects of covariates on the surrogate and true endpoint to vary across trials.
46
Swingler, Kevin. "Learning and Searching Pseudo-Boolean Surrogate Functions from Small Samples." Evolutionary Computation 28, no. 2 (June 2020): 317–38. http://dx.doi.org/10.1162/evco_a_00257.
Повний текст джерелаАнотація:
When searching for input configurations that optimise the output of a system, it can be useful to build a statistical model of the system being optimised. This is done in approaches such as surrogate model-based optimisation, estimation of distribution algorithms, and linkage learning algorithms. This article presents a method for modelling pseudo-Boolean fitness functions using Walsh bases and an algorithm designed to discover the non-zero coefficients while attempting to minimise the number of fitness function evaluations required. The resulting models reveal linkage structure that can be used to guide a search of the model efficiently. It presents experimental results solving benchmark problems in fewer fitness function evaluations than those reported in the literature for other search methods such as EDAs and linkage learners.
47
Protonotarios, Nicholas E., George A. Kastis, Andreas D. Fotopoulos, Andreas G. Tzakos, Dimitrios Vlachos, and Nikolaos Dikaios. "Motion-Compensated PET Image Reconstruction via Separable Parabolic Surrogates." Mathematics 11, no. 1 (December 23, 2022): 55. http://dx.doi.org/10.3390/math11010055.
Повний текст джерелаАнотація:
The effective resolution of positron emission tomography (PET) can be significantly degraded by patient motion during data acquisition. This is especially true in the thorax due to respiratory motion. This study concentrates on the improvement of motion correction algorithms both in terms of image quality and computational cost. In this paper, we present a novel motion-compensated image reconstruction (MCIR) algorithm based on a parabolic surrogate likelihood function instead of the loglikelihood function of the expectation maximization (EM) algorithm. The theoretical advantage of the parabolic surrogate algorithm lies within the fact that its loglikelihood is upper bounded by the EM loglikelihood, thus it will converge faster than EM. This is of particular importance in PET motion correction, where reconstructions are very computationally demanding. Relaxation parameters were also introduced to converge closer to the maximum likelihood (ML) solution and achieve lower noise levels. Image reconstructions with embedded relaxation parameters actually converged to better solutions than the corresponding ones without relaxation. Motion-compensated parabolic surrogates were indeed shown to accelerate convergence compared to EM, without reaching a limit cycle. Nonetheless, with the incorporation of ordered subsets in the reconstruction setting, the improvement was less evident.
48
Candida Fratazzi and Jixiao Niu. "Accelerated orphan drug approval: surrogate endpoints." World Journal of Advanced Pharmaceutical and Medical Research 2, no. 1 (January 30, 2022): 001–7. http://dx.doi.org/10.53346/wjapmr.2022.2.1.0021.
Повний текст джерелаАнотація:
Today, orphan drug development is confronted with significant challenges represented by the considerable complexity, diversity of clinical manifestations, and competition in study recruitment. Thus, surrogate endpoints adoption plays a crucial role in rare disease trials by minimizing costs, the number of subjects, and study duration. Surrogate endpoints, to substitute for a direct measure of how patients feel, function, or survive, must be biomarkers that directly correlate with disease clinical manifestations and predict the impact of study drug on the long-term disease progression. Validation of surrogate endpoints for accuracy and sensitivity is essential to maximize its benefit and utility. These validation criteria include reliability, reproducibility, keenness, and a direct reflection of patient feeling, function, or survival upon treatment. On the other hand, the selection of surrogate endpoints may be pretty complex, and making mistakes may lead to inaccurate estimates of the clinical benefit. Finally, surrogate endpoints contribute to a composite endpoint when studying drug benefits patients in multiple ways, and not all the measured components are detected in each patient. Furthermore, a composite endpoint comprised of multiple surrogate endpoints could improve statistical efficiency. Selection and qualification of biomarkers for surrogate endpoints and accelerated market approval is often a complex process that requires experience and method. Today, we contributed to developing orphan drugs for many rare diseases, including Neurological Diseases, Lysosomal Storage Diseases, Metabolic Conditions, Immune Disorders, and Cancers. In conclusion, surrogate endpoints play an essential role in orphan drug development, benefiting patients and the healthcare system.
49
Kamali, M., K. Ponnambalam, and E. D. Soulis. "Computationally efficient calibration of WATCLASS Hydrologic models using surrogate optimization." Hydrology and Earth System Sciences Discussions 4, no. 4 (July 23, 2007): 2307–21. http://dx.doi.org/10.5194/hessd-4-2307-2007.
Повний текст джерелаАнотація:
Abstract. In this approach, exploration of the cost function space was performed with an inexpensive surrogate function, not the expensive original function. The Design and Analysis of Computer Experiments(DACE) surrogate function, which is one type of approximate models, which takes correlation function for error was employed. The results for Monte Carlo Sampling, Latin Hypercube Sampling and Design and Analysis of Computer Experiments(DACE) approximate model have been compared. The results show that DACE model has a good potential for predicting the trend of simulation results. The case study of this document was WATCLASS hydrologic model calibration on Smokey-River watershed.
50
Na, Chongzheng, and Huixin Liu. "A Historical Experience Surrogate Model Assisted Particle Swarm Optimization for Expensive Black-box Problems." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 83–88. http://dx.doi.org/10.54097/hset.v7i.1021.
Повний текст джерелаАнотація:
This paper proposed a new historical experience surrogate model assisted particle swarm optimization method. This method extends the particle swarm optimization by adding new surrogate-based phase. In the classic phase, the particle swarm optimization runs the same way as the original algorithm, and the real function value evaluated are collected into the global database. In the surrogate phase, sub-swarms are generated following the distribution of the history data and evaluated by the surrogate model(s). The purpose of the surrogate phase is to explore the possible better solutions of the searching history. Also, the surrogate model(s) have the ability of accelerating the intelligence algorithms. Nevertheless, considering the time complexity of training and evaluating the surrogate model(s), the original problem should be expensive to evaluate or driven by data, which are same as many real-world problems.