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Genetic and Evolutionary Computation Conference (GECCO)

作者： Akimoto, Youhei Univ Tsukuba Tsukuba Ibaraki Japan RIKEN AIP Tsukuba Ibaraki Japan

ISBN: (纸本)9781450392372

A surrogate function is often employed to reduce the number of objective function evaluations for optimization. However, the effect of using a surrogate model in evolutionary approaches has not been theoretically investigated. This paper theoretically analyzes the information-geometric optimization framework using a surrogate function. The value of the expected objective function under the candidate sampling distribution is used as the measure of progress of the algorithm. We assume that the surrogate function is maintained so that the population version of the Kendall's rank correlation coefficient between the surrogate function and the objective function under the candidate sampling distribution is greater than or equal to a predefined threshold. We prove that informationgeometric optimization using such a surrogate function leads to a monotonic decrease in the expected objective function value if the threshold is sufficiently close to one. The acceptable threshold value is analyzed for the case of the information-geometric optimization instantiated with Gaussian distributions, i.e., the rank-.. update CMA-ES, on a convex quadratic objective function. As an alternative to the Kendall's rank correlation coefficient, we investigate the use of the Pearson correlation coefficient between the weights assigned to candidate solutions based on the objective function and the surrogate function.

关键词： information-geometric optimization covariance matrix adaptation evolution strategy surrogate function Kendall's rank correlation coefficient Pearson's correlation coefficient monotone improvement

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A surrogate function for One-Dimensional Phylogenetic Likelihoods

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MOLECULAR BIOLOGY AND EVOLUTION 2018年第1期35卷 242-246页

作者： Claywell, Brian C. Vu Dinh Fourment, Mathieu McCoy, Connor O. Matsen, Frederick A. Fred Hutchinson Canc Res Ctr Program Computat Biol 1124 Columbia St Seattle WA 98104 USA Univ Delaware Dept Math Sci Newark DE 19716 USA Univ Technol Sydney Inst 3 Ultimo NSW Australia

Phylogenetics has seen a steady increase in data set size and substitution model complexity, which require increasing amounts of computational power to compute likelihoods. This motivates strategies to approximate the likelihood functions for branch length optimization and Bayesian sampling. In this article, we develop an approximation to the 1D likelihood function as parametrized by a single branch length. Our method uses a four-parameter surrogate function abstracted from the simplest phylogenetic likelihood function, the binary symmetric model. We show that it offers a surrogate that can be fit over a variety of branch lengths, that it is applicable to a wide variety of models and trees, and that it can be used effectively as a proposal mechanism for Bayesian sampling. The method is implemented as a stand-alone open-source C library for calling from phylogenetics algorithms;it has proven essential for good performance of our online phylogenetic algorithm sts.

关键词： phylogenetic likelihood surrogate function Bayesian phylogenetics proposal distribution

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Image reconstruction method for incomplete CT projection based on self-guided image filtering

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第7期62卷 2101-2116页

作者： Song, Qiang Gong, Changcheng Chongqing Technol & Business Univ Sch Math & Stat Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Chongqing Key Lab Stat Intelligent Comp & Monitori Chongqing 400067 Peoples R China

In some fields of medical diagnosis or industrial nondestructive testing, it is difficult to obtain complete computed tomography (CT) data due to the limitation of radiation dose or other factors. Therefore, image reconstruction of incomplete projection data is the focus of this paper. In this paper, a new image reconstruction model based on self-guided image filtering (SGIF) term is proposed for few-view and segmental limited-angle (SLA) CT reconstruction. Then the alternating direction method (ADM) is used to solve this model. For simplicity, we call it ADM-SGIF method. The key idea of ADM-SGIF method is to use the reconstructed image itself as a reference and utilize its structural features to guide CT reconstruction. This method can effectively preserve image structures and remove shading artifacts. To validate the effectiveness of the proposed reconstruction method, we conduct digital phantom and real CT data experiments. The results indicate that ADM-SGIF method outperforms competing methods, including total variation (TV), relative total variation (RTV), and L0-norm minimization solved by ADM (ADM-L0) methods, in both subjective and objective evaluations.

关键词： Computed tomography Segmental limited-angle Self-guided image filtering Guided image filtering surrogate function

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Analysis of surrogate-Assisted Information-Geometric Optimization Algorithms

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ALGORITHMICA 2024年第1期86卷 33-63页

作者： Akimoto, Youhei Univ Tsukuba Fac Engn Informat & Syst 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan RIKEN Ctr Adv Intelligence Project 1-4-1 NihonbashiChuo ku Tokyo 1030027 Japan

surrogate functions are often employed to reduce the number of objective function evaluations in a continuous optimization. However, their effects have seldom been investigated theoretically. This paper analyzes the effect of a surrogate function in the information-geometric optimization (IGO) framework, which includes as an algorithm instance a variant of the covariance matrix adaptation evolution strategy-a widely used solver for black-box continuous optimization. We derive a sufficient condition on the surrogate function for the parameter update in the IGO algorithms to point to a descent direction of the objective function expected over the search distribution. The condition is expressed in terms of three measures of correlation between the objective function and the surrogate function. Our result constitutes a partial justification for the use of a surrogate function in IGO algorithms.

关键词： Information-geometric optimization Covariance matrix adaptation evolution strategy surrogate function Correlation measure Descent condition

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Techno-economic analysis of membrane-based continuous capture chromatography platforms for large-scale antibody production

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BIOTECHNOLOGY PROGRESS 2025年

作者： Romero, Juan J. Jenkins, Eleanor W. Birtwistle, Marc R. Husson, Scott M. Clemson Univ Dept Chem & Biomol Engn Clemson SC 29634 USA Clemson Univ Sch Math & Stat Sci Clemson SC USA

Continuous manufacturing platforms and membrane chromatography are process technologies with the potential to reduce production costs and minimize process variability in monoclonal antibody production. This study presents a simulation and optimization framework to perform techno-economic analyses of these strategies. Multi-objective optimization was used to compare batch and continuous multicolumn operating modes and membrane and resin process alternatives, revealing performance differences in productivity and cost of goods attributed to variations in dynamic binding capacity, media geometry, and process residence time. From the set of optimal process configurations, we selected one membrane and one resin platform alternative yielding the highest net present values to undergo sensitivity analyses involving variations in batch cadence and product selling price. For the scenarios considered in this work, membrane continuous platforms showed benefits in the cost of goods and process mass intensity. Their shorter residence time compared to resins positions them as a viable alternative for single-use capture chromatography. Moreover, this low residence time makes membrane platforms more flexible to changes in throughput, an essential feature for integrating capture into fully continuous processes.

关键词： economic analysis LASSO-regression monoclonal antibodies multi-objective optimization surrogate function

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Machine learning-based surrogate models for predicting the optimized weight and prestress level of double-curvature cable domes

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THIN-WALLED STRUCTURES 2024年 203卷

作者： Ahmed, Elshaimaa A. El Damatty, Ashraf A. Western Univ Dept Civil & Environm Engn London ON Canada Mansoura Univ Dept Struct Engn Mansoura Egypt

A major challenge in designing cable domes is the high sensitivity of these lightweight flexible structures to geometry change. Including shape parameters with size of all elements and prestress level as discrete design variables in optimization algorithm may lead it to be computationally prohibitive. In this regard, this study employs and compares a multi-linear regression (MLR) model, a multiplicative non-linear regression (MNLR) model, and an artificial neural network (ANN) model with the aim to estimate the optimized prestress level at the minimum weight for different geometries of double-curvature cable domes. The models are trained using a large dataset of dome geometries developed and optimized numerically. The models show high accuracy with a slight advantage for ANN over MLR and MNLR. However, the prediction functions using MNLR model are much simpler and concise with only 7 coefficients when compared to a complete quadratic function of 28 terms needed in the MLR model and to the complexity of ANN equation. Moreover, unlike MLR and ANN, the MNLR model can synthetically generate a larger dataset of optimized values quite accurately beyond the trained range. The developed functions facilitate the prediction of the optimized prestress level and structural weight of doublecurvature cable domes when compared to the traditional optimization algorithms. They can be utilized as a design support tool when implemented as a built-in library in design software. They also save time and effort when employed as an objective function for a shape optimization problem. Moreover, they fulfill designers' preferences for the dome stiffness and load-bearing capacity. Finally, the impact of considered geometrical parameters on the optimized values is explored using the trained dataset, providing recommendations for designers.

关键词： surrogate function Cable domes ANN model Regression Prestress Optimized weight

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A Single- and Multi-objective Optimization Algorithm for Electromagnetic Devices Assisted by Adaptive Kriging Based on Parallel Infilling Strategy

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第1期16卷 301-308页

作者： Xia, Bin Liu, Ren He, Zhiwei Koh, Chang-Seop Shenyang Univ Technol Sch Elect Engn Shenyang Peoples R China Guizhou Aerosp Linquan Motor Co Ltd Guiyang Peoples R China Chungbuk Natl Univ Coll Elect & Comp Engn Cheongju South Korea

A computationally efficient surrogate model is suggested to approximate the objective and constraint function values, which replace expensive evaluation of the objective and constraint function values in numerical simulation-based optimization. Kriging surrogate model has been widely used in surrogate-based design optimization (SBDO) to replace the highly nonlinear black-box functions. In this paper, a novel adaptive Kriging model based on parallel infilling strategy is proposed to improve both the numerical accuracy and efficiency of the SBDO methods. The parallel infilling strategy consists of two parts: local sampling and globaluthor sampling. In the local sampling, new additional sampling points are generated only within a limited region that is determined according to the optimal point at the last iteration, while in global sampling they are generated based on the fitting error estimation in the whole region. The effectiveness of the proposed algorithm is verified through applications to analytical functions. Then the algorithm is applied to the multi-objective optimal design of an ironless permanent magnet synchronous linear motor.

关键词： Kriging Fitting error Infilling strategy Multi-objective optimization surrogate function

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A comprehensive framework based on Bayesian optimization and skip connections artificial neural networks to predict buildings energy performance

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JOURNAL OF BUILDING ENGINEERING 2023年 77卷

作者： Abbass, Mahmoud Abdelkader Bashery Chungbuk Natl Univ Sch Elect Engn 1 Chungdae Ro Cheongju 28644 South Korea Helwan Univ Dept Mech Power Engn Cairo 11772 Egypt

Artificial Neural Networks (ANNs) are a powerful modeling and prediction tool. However, in the energy and buildings fields, certain articles have been highlighted for reducing ANNs' ability to aid in building energy prediction. This result stems from the author's limited ability to regulate a large number of hyper-parameters. As a result, this paper provides a comprehensive study of ANN challenges and solutions in a single framework. The study's research aims may be stated as defining the necessary data preparation stages;selecting the appropriate ANN design for each study scenario with its hyper-parameters;and presenting the approach for training and evaluating ANNs to obtain the best performance. All of these concerns are presented in this study, compiled into a comprehensive framework, and implemented on two different types of real-life data: 1) CBECS data for US commercial buildings, and 2) ASHRAE data for different buildings around the world. The framework optimization loop mainly relies on the Bayesian optimization technique, which employs Gaussian Processes (GP) as a surrogate function;a combination of two concepts (i. e., Expected Improvement (EI) and Probability Improvement (PI)) as an acquisition function;hyper-parameters as a dimension space optimization;and adjusted R2 value as a target optimization score to be improved. Various skip connection ANN architectures are applied to demonstrate the ability to modify ANNs' limitations in the energy and building fields, assuring excellent accuracy and reliability in forecasting building energy performance, with adjusted R2 of more than 0.93 for both data sets.

关键词： Machine learning Artificial neural networks Skip connection Building energy performance simulation Hyper-parameters tuning Bayesian optimization surrogate function Acquisition function CBECS data ASHRAE data

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An Majorize-Minimize algorithm framework for large scale truncated loss classifiers

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Procedia Computer Science 2022年 214卷 552-559页

作者： Xiaofang Li Lingwei Huang Yuanhai Shao School of Science Hainan University Haikou 570228 China Management School Hainan University Haikou 570228 China

The truncated losses, the truncations of the convex losses, have been widely applied in classification tasks due to their robustness. However, the corresponding non-convex optimization problems are hard to solve efficiently. In this paper, we propose an algorithm framework for the truncated loss classifiers by finding a type of surrogate integral loss of the truncated losses. We show that the surrogate integral loss is a computationally efficient equivalent of the truncated loss. Thereafter our algorithm framework is present to solve the truncated loss model, and allows us to obtain the critical point of the truncated loss model by solving a series of the corresponding original convex loss models. In fact, for any solvable original convex loss model, the corresponding truncated loss model can be solved by our algorithm in linear and nonlinear cases. Experiments on benchmark datasets show that our algorithm framework gets high efficiency if the original convex loss models can be solved.

关键词： Truncated losses surrogate function logistic regression (LR) support vector machine (SVM)

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Faster Hyperparameter Optimization via Finding Minimal Regions in Random Forest Regressor

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Procedia Computer Science 2022年 212卷 378-386页

作者： Viacheslav Shalamov Valeria Efimova Andrey Filchenkov ITMO University

In optimisation problems, it is often more convenient and efficient to approximate a target function with another function, which would be much easier to compute or analyse. Such approximations are called surrogate functions. A surrogate function can also be a fairly complex object itself. For example, a Random Forest Regressor (RFR) can be used to approximate target function in Hyperparameter Optimisation processes. Hyperparameter vectors define the feature space, and target algorithm performance is considered the target function. Thus, RFR trained on a set of examples, can approximate algorithm performance, using hyperpa-rameter vectors. However, it is hard to fnd its minima. Thus, “black-box” optimization algorithms are applied for optimization purposes, such as random or local searches. In this paper, we propose a method ShortestForestSearch to search RFR minimum points using branch and bound approach. We conducted several experiments for comparing the algorithm proposed with näıve approaches. ShortestForestSearch demonstrated significant performance improvements and the potential to improve other algorithms which use Random Forest internally as a surrogate function. Based on this, we propose ShortestForestSMAC, a new algorithm for hyperparameter optimization. It is based on the well-known SMAC algorithm and implements idea of ShortestForestSearch for selecting best new candidates on each optimization iteration. Experimental evaluation shows that ShortestForestSMAC outperforms SMAC by achieving similar results in considerably shorter time span.

关键词： Random forest Minimization Hyperparameters optimization surrogate function Branch and bound

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