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Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites

EVOLUTIONARY COMPUTATION 2022年第2期30卷 165-193页

作者： Brockhoff, Dimo Auger, Anne Hansen, Nikolaus Tusar, Tea Inst Polytech Paris Ecole Polytech CNRS InriaCMAP Paris France Jozef Stefan Inst Ljubljana Slovenia

Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, such as well-understood Pareto sets and Pareto fronts of various shapes, most of the currently used functions possess characteristics that are arguably underrepresented in real-world problems such as separability, optima located exactly at the boundary constraints, and the existence of variables that solely control the distance between a solution and the Pareto front. Via the alternative construction of combining existing single-objective problems from the literature, we describe the bbob-biobj test suite with 55 bi-objective functions in continuous domain, and its extended version with 92 bi-objective functions (bbob-biobj-ext). Both test suites have been implemented in the COCO platform for black-box optimization benchmarking and various visualizations of the test functions are shown to reveal their properties. Besides providing details on the construction of these problems and presenting their (known) properties, this article also aims at giving the rationale behind our approach in terms of groups of functions with similar properties, objective space normalization, and problem instances. The latter allows us to easily compare the performance of deterministic and stochastic solvers, which is an often overlooked issue in benchmarking.

关键词： Black-box optimization benchmarking multiobjective optimization algorithm comparison benchmark suite generator

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Material analysis and big data monitoring of sports training equipment based on machine learning algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第4期34卷 2749-2763页

作者： Zhang, Lei Li, Ning Lanzhou Univ Technol Dept Phys Educ Lanzhou 730050 Gansu Peoples R China Hunan Univ Finance & Econ PE Dept Changsha 410205 Hunan Peoples R China

Different machine learning algorithms predict the application effect of perovskite materials in sports training equipment. The sensitivity to material data is different on different ranges of data sets. Therefore, the algorithm needs to be selected according to specific material data samples. This study compares the prediction performance of neural network prediction algorithm (NN), genetic algorithm, and support vector machine-based machine learning algorithm (SVM) and uses statistical analysis to perform data analysis and draw corresponding curves. Moreover, this study uses a single perovskite material to verify the algorithm performance. In addition, based on the real data, the three machine learning algorithms of this study are applied to the related performance prediction, and the comparative analysis method is used to analyze the prediction performance of the machine learning algorithm. Through data analysis and chart analysis, we can see that machine learning algorithms have a certain effect in the application prediction of perovskite materials in sports training equipment. Among the three machine learning algorithms selected in this study, the performance of the machine learning algorithm based on support vector machine in all aspects is more excellent.

关键词： Machine learning algorithm comparison Perovskite Material properties Prediction Physical training

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An Improved Cross-Validated Adversarial Validation Method 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Zhang, Wen Liu, Zhengjiang Xue, Yan Wang, Ruibo Cao, Xuefei Li, Jihong Shanxi Univ Sch Automat & Software Engn Taiyuan 030006 Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Shanxi Univ Sch Modern Educ Technol Taiyuan 030006 Peoples R China

ISBN: (数字)9783031402838

ISBN: (纸本)9783031402821;9783031402838

As a widely-used strategy among Kaggle competitors, adversarial validation provides a novel selection framework of a reasonable training and validation sets. An adversarial validation heavily depends on an accurate identification of the difference between the distributions of the training and test sets released in a Kaggle competition. However, the typical adversarial validation merely uses a K-fold cross-validated point estimator to measure the difference regardless of the variation of the estimator. Therefore, the typical adversarial validation tends to produce unpromising false positive conclusions. In this study, we reconsider the adversarial validation from a perspective of algorithm comparison. Specifically, we formulate the adversarial validation into a comparison task of a well-trained classifier with a random-guessing classifier on an adversarial data set. Then, we investigate the state-of-the-art algorithm comparison methods to improve the adversarial validation method for reducing false positive conclusions. We conducted sufficient simulated and real-world experiments, and we showed the recently-proposed 5 x 2 BCV McNemar's test can significantly improve the performance of the adversarial validation method.

关键词： Adversarial Validation Cross Validation algorithm comparison Significance Testing Distribution Shift

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CONSTRAINED BAYESIAN OPTIMIZATION METHODS USING REGRESSION AND CLASSIFICATION GAUSSIAN PROCESSES AS CONSTRAINTS

CONSTRAINED BAYESIAN OPTIMIZATION METHODS USING REGRESSION A...

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ASME International Design Engineering Technical Conferences / Computers and Information in Engineering Conference (IDETC-CIE) / 49th Design Automation Conference (DAC)

作者： Jetton, Cole Li, Chengda Hoyle, Christopher Oregon State Univ Design Engn Lab Corvallis OR 97331 USA

ISBN: (纸本)9780791887318

In Bayesian optimization, which is used for optimizing computationally expensive black-box problems, constraints need to be considered to arrive at solutions that are both optimal and feasible. Techniques to deal with black-box constraints have been studied extensively, often exploiting the constraint model to sample points that have a high probability of being feasible. These constraints are usually modeled using regression models if the output is a known value on a continuous real scale. However, if the constraint or set of constraints is not modeled explicitly, but rather it is only known whether a design vector is either feasible or infeasible, then we treat the constraints as a categorical constraint. Because of this, these constraints should be modeled using classification models rather than regression methods, which have also been studied. Because of the variety of approaches to handling constraints, there is a need to compare methods for handling both classification constraints as well as continuous constraints modeled with individual regression models. This paper explores and compares four main methods, with two additional ones specifically for classification constraints;these methods handle black-box constraints in Bayesian optimization by modeling the constraints with both regression and classification Gaussian processes. We found that the problem type and number of constraints influence the effectiveness of different approaches with statistical differences in convergence. Regression models can be advantageous in terms of model fit time;however, this is also a function of the total number of constraints. Generally, regression constraints outperformed classification surrogates in terms of minimizing computational time, but the latter was still effective. Overall, this study provides valuable insights into the performance and implementation of different constrained Bayesian optimization techniques. This can help inform engineers on which method is most suit

关键词： algorithm comparison Bayesian Optimization Constraint Handling Gaussian Process Classification Gaussian Process Regression Optimization.

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Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning algorithms 13

Depth Functions for Partial Orders with a Descriptive Analys...

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13th International Symposium on Imprecise Probabilities - Theories and Applications (ISIPTA)

作者： Blocher, Hannah Schollmeyer, Georg Jansen, Christoph Nalenz, Malte Ludwig Maximilians Univ Munchen Dept Stat Munich Germany

We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial orders, the union-free generic (ufg) depth. Moreover, we utilize our ufg depth for a comparison of machine learning algorithms based on multidimensional performance measures. Concretely, we analyze the distribution of different classifier performances over a sample of standard benchmark data sets. Our results promisingly demonstrate that our approach differs substantially from existing benchmarking approaches and, therefore, adds a new perspective to the vivid debate on the comparison of classifiers.

关键词： partial orders data depth benchmarking algorithm comparison outlier detection non-standard data

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Semi-supervised Learning in Distributed Split Learning Architecture and IoT Applications 15

Semi-supervised Learning in Distributed Split Learning Archi...

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IEEE 15th International Symposium on Autonomous Decentralized Systems (ISADS)

作者： Barhoush, Mahdi Ayad, Ahmad Schmeink, Anke Rhein Westfal TH Aachen Chair Informat Theory & Data Analyt Aachen Germany

ISBN: (纸本)9781665464512

In the era of big data, new learning techniques are emerging to solve the difficulties of data collection, storage, scalability, and privacy. To overcome these challenges, we propose a distributed learning system that merges the hybrid edge-cloud split-learning architecture with the semi-supervised learning scheme. The proposed system based on three semi-supervised learning algorithms (FixMatch, Virtual Adversarial Training, and MeanTeacher) is compared to the supervised learning scheme and trained on different datasets and data distributions (IID and non-IID) and with a variable number of clients. The new system could efficiently utilize the local unlabeled samples on the client side and gave a performance encouragement that exceeds 30% in most cases even with small percentage of labelled data. Additionally, certain Split-SSL algorithms showed performance that was on par with or occasionally even better than more resource-intensive algorithms, although requiring less processing power and convergence time.

关键词： algorithm comparison Big Data Distributed Learning Semi-supervised learning Split Learning

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Speeding up approximate MAP by applying domain knowledge about relevant variables 11

Speeding up approximate MAP by applying domain knowledge abo...

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International Conference on Probabilistic Graphical Models

作者： Kwisthout, Johan Radboud Univ Nijmegen Donders Inst Brain Cognit & Behav Nijmegen Netherlands

The MAP problem in Bayesian networks is notoriously intractable, even when approximated. In an earlier paper we introduced the Most Frugal Explanation heuristic approach to solving MAP, by partitioning the set of intermediate variables (neither observed nor part of the MAP variables) into a set of relevant variables, which are marginalized out, and irrelevant variables, which will be assigned a sampled value from their domain. In this study we explore whether knowledge about which variables are relevant for a particular query (i.e., domain knowledge) speeds up computation sufficiently to beat both exact MAP as well as approximate MAP while giving reasonably accurate results. Our results are inconclusive, but also show that this probably depends on the specifics of the MAP query, most prominently the number of MAP variables.

关键词： MAP problem approximate MAP heuristic algorithm comparison background knowledge

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A comprehensive comparison of accuracy and practicality of different types of algorithms for pre-impact fall detection using both young and old adults

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MEASUREMENT 2022年 201卷

作者： Yu, Xiaoqun Koo, Bummo Jang, Jaehyuk Kim, Youngho Xiong, Shuping Korea Adv Inst Sci & Technol KAIST Dept Ind & Syst Engn Daejeon 34141 South Korea Yonsei Univ Dept Biomed Engn Wonju 26493 South Korea

This study aims to comprehensively compare the accuracy and practicality of three different types of algorithms for pre-impact fall detection using both young and old subjects. Threshold-based, conventional machine learning (SVM) and deep learning (ConvLSTM) algorithms were compared. Results showed that ConvLSTM had an ac-curacy of 99.16 % (sensitivity: 99.32 %, specificity: 99.01 %) and an averaged lead time of 403 ms on young subjects, which outperformed SVM (97.16 %, 385 ms) and much superior to the threshold-based algorithm (89.06 %, 333 ms). In addition, latency tests on an embedded device showed that the Lite model of ConvLSTM had a low latency of 2.1 ms, which was comparable to the threshold-based algorithm (<1 ms) but much lower than SVM (86.9 ms). The feasibility and effectiveness of applying algorithms trained on young subjects to old subjects were also validated. These findings suggested that ConvLSTM has great potential for detecting pre -impact falls and preventing fall-related injuries.

关键词： algorithm comparison Fall risk Inertial sensor Machine learning Pre -impact fall detection

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Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2024年

作者： Yan, Bo Guo, Yonghuan Fan, Xiying Zhao, Jinyue Lu, Xiangning Zhang, Xinran Ma, Lihang Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou Peoples R China Jiangsu Normal Univ JSNU SPbPU Inst Engn Xuzhou Peoples R China Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

关键词： P92 steel laser welding multiobjective optimization algorithm comparison

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Effects of Medical Clothing on Person Re-Identification algorithms 17

Effects of Medical Clothing on Person Re-Identification Algo...

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17th IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA)

作者： Kohout, Lukas Scheerer, Jan Zimmermann, Christoph Stork, Wilhelm FZI Reaserch Ctr Informat Technol Embedded Syst & Sensors Engn Karlsruhe Germany Karlsruhe Inst Technol KIT Inst Informat Proc Technol Karlsruhe Germany

ISBN: (数字)9781665482998

ISBN: (纸本)9781665482998

Accurate camera-based human action recognition over longer periods of time or in different camera views requires re-identification of individuals to correctly associate the actions. This is especially important if you want to track people's actions over time. Most work in person re-identification currently focuses on improving the performance of re-identification models for images of people wearing everyday clothing. This becomes a problem when the re-identification scenario changes, and with it the typical appearance of people in that specific environment. Therefore, this work examines the effects of medical clothing on five different person re-identification algorithms. As artificial intelligence and computer vision find more and more applications in the medical field, the question arises to what extent current implementations of person re-identification algorithms are able to generalize from non-medical data, so that the algorithms can be applied in a medical scenario. Since person re-identification is a well-studied topic in the computer vision community, and can also be used in medical settings, this work focuses on the impact of medical clothing on such algorithms. This becomes relevant because the medical clothing is highly uniform and covers many features of a person's characteristics. In addition to examining the effects of clothing as described, ways to overcome the resulting limitations are discussed. In the absence of medical datasets for person re-identification, a suitable dataset was generated containing images of people in medical clothing and the required annotations. Five different existing re-identification models were trained on a non-medical dataset and then tested with the medical data created for this study. The results show a general drop in performance when subjects are wearing medical clothing instead of normal cloths. By additionally marking all people with individual colored hairnets, the re-identification performance can be improved comp

关键词： person re-identification medical clothing medical environment uniform clothing individual markers ReID generalization computer vision algorithm comparison OR monitoring

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