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Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features 26th

Nullifying the Inherent Bias of Non-invariant Exploratory ...

26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Prager, Raphael Patrick Trautmann, Heike Data Science: Statistics and Optimization University of Münster Münster Germany Data Management and Biometrics University of Twente Enschede Netherlands

ISBN: (纸本)9783031302282

Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a comprehensive and large set of numerical features characterizing problem instances. Those foster problem understanding and serve as basis for constructing automated algorithm selection models choosing the best suited algorithm for a problem at hand based on the aforementioned features computed prior to optimization. This work specifically points to the sensitivity of a substantial proportion of these features to absolute objective values, i.e., we observe a lack of shift and scale invariance. We show that this unfortunately induces bias within automated algorithm selection models, an overfitting to specific benchmark problem sets used for training and thereby hinders generalization capabilities to unseen problems. We tackle these issues by presenting an appropriate objective normalization to be used prior to ELA feature computation and empirically illustrate the respective effectiveness focusing on the BBOB benchmark set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： optimization

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Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP

Using Reinforcement Learning for Per-Instance Algorithm Conf...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Vinzent Seiler, Moritz Rook, Jeroen Heins, Jonathan Ludger Preub, Oliver Bossek, Jakob Trautmann, Heike University of Twente Data Management and Biometrics Enschede Netherlands Tu Dresden Big Data Analytics in Transportation Dresden Germany Aachen University Chair for Ai Methodology Rwth Aachen Germany University of Münster Data Science: Statistics and Optimization Münster Germany University of Twente Data Science: Statistics and Optimization Enschede Netherlands

ISBN: (纸本)9781665430654

Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin-Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100 000 TSP instances with 2 000 nodes each - currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets. © 2023 IEEE.

关键词： Deep Reinforcement Learning Per-Instance Algorithm Configuration Traveling Salesperson Problem

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Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets 12th

Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchm...

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12th International Conference on Evolutionary Multi-Criterion optimization, EMO 2023

作者： Schäpermeier, Lennart Kerschke, Pascal Grimme, Christian Trautmann, Heike Big Data Analytics in Transportation TU Dresden & ScaDS.AI Dresden Germany Data Science: Statistics and Optimization University of Münster Münster Germany

ISBN: (纸本)9783031272493

The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. However, they lack diversity in their landscape properties and do not provide a mechanism for creating multiple distinct problem instances. Other suites, like bi-objective BBOB, possess diverse and challenging landscape properties, but their optima are not well understood and can only be approximated empirically without any guarantees. This work proposes a methodology for creating complex continuous problem landscapes by concatenating single-objective functions from version 2 of the multiple peaks model (MPM2) generator. For the resulting problems, we can determine the distribution of optimal points with arbitrary precision w.r.t. a measure such as the dominated hypervolume. We show how the properties of the MPM2 generator influence the multi-objective problem landscapes and present an experimental proof-of-concept study demonstrating how our approach can drive well-founded benchmarking of MO algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Facial expression recognition under occlusion conditions based on multi-feature cross-attention

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

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Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP

Using Reinforcement Learning for Per-Instance Algorithm Conf...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Moritz Vinzent Seiler Jeroen Rook Jonathan Heins Oliver Ludger Preuß Jakob Bossek Heike Trautmann Data Science: Statistics and Optimization University of Münster Münster Germany Data Management and Biometrics University of Twente Enschede Netherlands Big Data Analytics in Transportation TU Dresden Dresden Germany Chair for AI Methodology RWTH Aachen University Aachen Germany Data Science: Statistics and Optimization University of Twente Enschede Netherlands

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Advancements on multi-fidelity random Fourier neural networks: application to hurricane modeling for offshore wind energy

Advancements on multi-fidelity random Fourier neural network...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Davis, Owen Geraci, Gianluca Wentz, Jacqueline M. King, Ryan N. Cortiella, Alexandre Rybchuk, Alex Gomez, Miguel Sanchez Deskos, Georgios Motamed, Mohammad Optimization and Uncertainty Quantification Sandia National Laboratories AlbuquerqueNM United States Computational Data Science Sandia National Laboratories LivermoreCA United States National Wind Technology Center National Renewable Energy Laboratory GoldenCO United States Department of Applied Mathematics and Statistics University of New Mexico AlbuquerqueNM United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Large eddy simulation

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Numerical Methods for a Class of Quadratic Matrix Equations

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应用数学 2024年第4期37卷 962-970页

作者： GUAN Jinrui WANG Zhixin SHAO Rongxia School of Mathematics and Statistics Taiyuan Normal UniversityJinzhong 030619China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology Taiyuan Normal UniversityJinzhong 030619China School of Statistics and Data Science Xinjiang University of Finance and EconomicsUrumqi 830012China

Quadratic matrix equations arise in many elds of scienti c computing and engineering *** this paper,we consider a class of quadratic matrix *** a certain condition,we rst prove the existence of minimal nonnegative solution for this quadratic matrix equation,and then propose some numerical methods for solving *** analysis and numerical examples are given to verify the theories and the numerical methods of this paper.

关键词： Quadratic matrix equation M-matrix Minimal nonnegative solution Newton method Bernoulli method

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A COLLECTION OF DEEP LEARNING-BASED FEATURE-FREE APPROACHES FOR CHARACTERIZING SINGLE-OBJECTIVE CONTINUOUS FITNESS LANDSCAPES

arXiv

引用

arXiv 2022年

作者： Seiler, Moritz Vinzent Prager, Raphael Patrick Kerschke, Pascal Trautmann, Heike Data Science: Statistics and Optimization University of Münster Germany Big Data Analytics in Transportation Tu Dresden Germany University of Twente Netherlands

Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection. © 2022, CC BY.

关键词： optimization

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A goodness-of-fit test for functional time series with applications to Ornstein-Uhlenbeck processes

arXiv

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arXiv 2022年

作者： Álvarez-Liébana, J. López-Pérez, Alejandra González-Manteiga, W. Febrero-Bande, M. Department of Statistics and Data Science Faculty of Statistics Complutense University of Madrid Spain Department of Statistics Mathematical Analysis and Optimization Universidade de Santiago de Compostela Spain

High-frequency financial data can be collected as a sequence of curves over time;for example, as intra—day price, currently one of the topics of greatest interest in finance. The Functional data Analysis framework provides a suitable tool to extract the information contained in the shape of the daily paths, often unavailable from classical statistical methods. In this paper, a novel goodness-of-fit test for autoregressive Hilbertian (ARH) models, with unknown and general order, is proposed. The test imposes just the Hilbert-Schmidt assumption on the functional form of the autocorrelation operator, and the test statistic is formulated in terms of a Cramér–von Mises norm. A wild bootstrap resampling procedure is used for calibration, such that the finite sample behavior of the test, regarding power and size, is checked via a simulation study. Furthermore, we also provide a new specification test for diffusion models, such as Ornstein-Uhlenbeck processes, illustrated with an application to intra-day currency exchange rates. In particular, a two-stage methodology is proffered: firstly, we check if functional samples and their past values are related via ARH(1) model;secondly, under linearity, we perform a functional F-test. © 2022, CC BY-NC-ND.

关键词： Time series

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Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box optimization

arXiv

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arXiv 2024年

作者： Dietrich, Konstantin Prager, Raphael Patrick Doerr, Carola Trautmann, Heike Big Data Analytics in Transportation TU Dresden Germany ScaDS.AI Dresden Germany Data Science: Statistics and Optimization University of Münster Germany Sorbonne Université CNRS LIP6 Paris France Machine Learning and Optimisation Paderborn University Germany Data Management and Biometrics Group University of Twente Netherlands

Exploratory landscape analysis (ELA) is a well-established tool to characterize optimization problems via numerical features. ELA is used for problem comprehension, algorithm design, and applications such as automated algorithm selection and configuration. Until recently, however, ELA was limited to search spaces with either continuous or discrete variables, neglecting problems with mixed variable types. This gap was addressed in a recent study that uses an approach based on target-encoding to compute exploratory landscape features for mixed-variable problems. In this work, we investigate an alternative encoding scheme based on SHAP values. While these features do not lead to better results in the algorithm selection setting considered in previous work, the two different encoding mechanisms exhibit complementary performance. Combining both feature sets into a hybrid approach outperforms each encoding mechanism individually. Finally, we experiment with two different ways of meta-selecting between the two feature sets. Both approaches are capable of taking advantage of the performance complementarity of the models trained on target-encoded and SHAP-encoded feature sets, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization

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