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Population-based evolutionary search for joint hyperparameter and architecture optimization in brain-computer interface

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 264卷

作者： Shin, Dong-Hee Lee, Deok-Joong Han, Ji-Wung Son, Young-Han Kam, Tae-Eui Korea Univ Dept Artificial Intelligence Seoul 02841 South Korea

In recent years, deep learning (DL)-based models have become the de facto standard for motor imagery brain-computer interface (MI-BCI) systems due to their notable performance. However, these models often require extensive hyperparameter optimization process to achieve optimal results. To tackle this challenge, recent studies have proposed various methods to automate this process. Despite promising results, these methods overlook the architecture elements, which are crucial factors for MI-BCI system performance and are highly intertwined with hyperparameter settings. To overcome this limitation, we propose a joint optimization framework that uses a population-based evolutionary search to optimize both hyperparameters and architectures. Our framework adopts a two-stage optimization approach that alternates between hyperparameter and architecture optimization to effectively manage the complexity of the joint search process. Furthermore, we introduce a novel ensemble method that leverages diverse promising configurations to enhance generalization and robustness. Evaluations on two public MI-BCI datasets show that our framework consistently outperforms competing methods across a range of backbone models, demonstrating its effectiveness and versatility.

关键词： Hyperparameter optimization architecture optimization Evolutionary search Joint optimization Brain-computer interface

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System architecture optimization strategies: dealing with expensive hierarchical problems

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JOURNAL OF GLOBAL optimization 2025年第4期91卷 851-895页

作者： Bussemaker, Jasper H. Saves, Paul Bartoli, Nathalie Lefebvre, Thierry Lafage, Remi German Aerosp Ctr DLR Inst Syst Architectures Aeronaut Hein Sass Weg 22 D-21129 Hamburg Germany Univ Toulouse Federat ENAC ISAE SUPAERO ONERA 2 Ave Edouard Belin F-31000 Toulouse France Univ Toulouse Federat ENAC ISAE SUPAERO ONERA 2 Marc Pelegrin F-31000 Toulouse France

Choosing the right system architecture for the problem at hand is challenging due to the large design space and high uncertainty in the early stage of the design process. Formulating the architecting process as an optimization problem may mitigate some of these challenges. This work investigates strategies for solving system architecture optimization (SAO) problems: expensive, black-box, hierarchical, mixed-discrete, constrained, multi-objective problems that may be subject to hidden constraints. Imputation ratio, correction ratio, correction fraction, and max rate diversity metrics are defined for characterizing hierarchical design spaces. This work considers two classes of optimization algorithms for SAO: multi-objective evolutionary algorithms such as NSGA-II, and Bayesian optimization (BO) algorithms. A new Gaussian process kernel is presented that enables modeling hierarchical categorical variables, extending previous work on modeling continuous and integer hierarchical variables. Next, a hierarchical sampling algorithm that uses design space hierarchy to group design vectors by active design variables is developed. Then, it is demonstrated that integrating more hierarchy information in the optimization algorithms yields better optimization results for BO algorithms. Several realistic single-objective and multi-objective test problems are used for investigations. Finally, the BO algorithm is applied to a jet engine architecture optimization problem. This work shows that the developed BO algorithm can effectively solve the problem with one order of magnitude less function evaluations than NSGA-II. The algorithms and problems used in this work are implemented in the open-source Python library SBArchOpt.

关键词： architecture optimization Bayesian optimization Hierarchical Multi-objective Mixed-discrete Hidden constraints

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architecture optimization: speed or accuracy? both!

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SOFTWARE QUALITY JOURNAL 2018年第2期26卷 661-684页

作者： Ciccozzi, Federico Feljan, Juraj Carlson, Jan Crnkovic, Ivica Malardalen Univ Sch Innovat Design & Engn Box 883 S-72123 Vasteras Sweden

Embedded systems are becoming more and more complex, thus demanding innovative means to tame their challenging development. Among others, early architecture optimization represents a crucial activity in the development of embedded systems to maximise the usage of their limited resources and to respect their real-time requirements. Typically, architecture optimization seeks good architecture candidates based on model-based analysis. Leveraging abstractions and estimates, this analysis usually produces approximations useful for comparing architecture candidates. Nonetheless, approximations do not provide enough accuracy in estimating crucial extra-functional properties. In this article, we provide an architecture optimization framework that profits from both the speed of model-based predictions and the accuracy of execution-based measurements. Model-based optimization rapidly finds a good architecture candidate, which is refined through optimization based on monitored executions of automatically generated code. Moreover, the framework enables the developer to leverage her optimization experience. More specifically, the developer can use runtime monitoring of generated code execution to manually adjust task allocation at modeling level, and commit the changes without halting execution. In the article, our architecture optimization mechanism is first described from a general point of view and then exploited for optimizing the allocation of software tasks to the processing cores of a multicore embedded system;we target extra-functional properties that can be concretely represented and automatically compared for different architectural alternatives (such as memory consumption, energy consumption, or response-time).

关键词： architecture optimization Software quality Model-driven engineering Model transformations Back-propagation Execution Monitoring Multicore Embedded systems

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architecture optimization OF AEROSPACE COMPUTING SYSTEMS

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IEEE TRANSACTIONS ON COMPUTERS 1983年第10期32卷 911-922页

作者： PEDAR, A SARMA, VVS INDIAN INST SCI SCH AUTOMATBANGALORE 560012KARNATAKAINDIA

Simultaneous consideration of both performance and reliability issues is important in the choice of computer architectures for real-time aerospace applications. One of the requirements for such a fault-tolerant computer system is the characteristic of graceful degradation. A shared and replicated resources computing system represents such an architecture. In this paper, a combinatorial model is used for the evaluation of the instruction execution rate of a degradable, replicated resources computing system such as a modular multiprocessor system. Next, a method is presented to evaluate the computation reliability of such a system utilizing a reliability graph model and the instruction execution rate. Finally, this computation reliability measure, which simultaneously describes both performance and reliability, is applied as a constraint in an architecture optimization model for such computing systems.

关键词： architecture optimization Computation Reliability Fault Tolerant Computing Replicated Resources Computing System Shared Resources Computing System Computer architecture Computational Modeling optimization Real Time Systems Throughput Fault Tolerance architecture optimization Computation Reliability Fault Tolerant Computing Replicated Resources Computing System Shared Resources Computing System

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architecture optimization with SysML Modeling: A Case Study Using Variability 3rd

Architecture Optimization with SysML Modeling: A Case Study ...

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3rd International Conference on Model-Driven Engineering and Software Development (MODELSWARD)

作者： Leserf, Patrick de Saqui-Sannes, Pierre Hugues, Jerome Chaaban, Khaled ESTACA Lab F-53000 Laval France Univ Toulouse ISAE SUPAERO F-31055 Toulouse France

ISBN: (纸本)9783319278698;9783319278681

Obtaining the set of trade-off architectures from a SysML model is an important objective for the system designer. To achieve this goal, we propose a methodology combining SysML with the variability concept and multi-objectives optimization techniques. An initial SysML model is completed with variability information to show up the different alternatives for component redundancy and selection from a library. The constraints and objective functions are also added to the initial SysML model, with an optimization context. Then a representation of a constraint satisfaction problem (CSP) is generated with an algorithm from the optimization context and solved with an existing solver. The paper illustrates our methodology by designing an Embedded Cognitive Safety System (ECSS). From a component repository and redundancy alternatives, the best design alternatives are generated in order to minimize the total cost and maximize the estimated system reliability.

关键词： architecture optimization SysML Embedded systems Model variability

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architecture optimization of radial basis function networks with a combination of hard- and soft-computing techniques

Architecture optimization of radial basis function networks ...

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IEEE International Conference on Systems, Man and Cybernetics (SMC 03)

作者： Klimek, M Sick, B Univ Passau D-94030 Passau Germany

ISBN: (纸本)0780379527

Feature selection and architecture optimization are two key tasks in many neural network applications. Appropriate input features must be selected from a given (and often large) set of possible features and architecture parameters of the network such as the number of hidden neurons or training parameters must be adapted with respect to the selected features and a data set given. This article describes an evolutionary algorithm (EA) that performs the two tasks simultaneously for radial basis function (RBF) networks applied to classification problems. In order to reduce the optimization effort significantly these soft-computing techniques are fused with various hard-computing techniques (e.g., clustering, solution of a linear least-squares problem, local search). The feasibility and the benefits of the approach are demonstrated by means of a data mining & knowledge discovery problem in the area of customer relationship management. The algorithm, however, is independent from specific applications such that the ideas and solutions may easily be transferred to other applications and even other neural network paradigms.

关键词： classification evolutionary algorithm network architecture optimization radial basis function networks feature selection data mining and knowledge discovery fusion of hard and soft-computing

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Domain-Specific Quantum architecture optimization

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2022年第3期12卷 624-637页

作者： Lin, Wan-Hsuan Tan, Bochen Niu, Murphy Yuezhen Kimko, Jason Cong, Jason Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA Google AI Quantum Venice CA 90291 USA

With the steady progress in quantum computing over recent years, roadmaps for upscaling quantum processors have relied heavily on the targeted qubit architectures. So far, similarly to the early age of classical computing, these designs have been crafted by human experts. These general-purpose architectures, however, leave room for customization and optimization, especially when targeting popular near-term QC applications. In classical computing, customized architectures have demonstrated significant performance and energy efficiency gains over general-purpose counterparts. In this paper, we present a framework for optimizing quantum architectures, specifically through customizing qubit connectivity. It is the first work that (1) provides performance guarantees by integrating architecture optimization with an optimal compiler, (2) evaluates the impact of connectivity customization under a realistic crosstalk error model, and (3) benchmarks on realistic circuits of near-term interest, such as the quantum approximate optimization algorithm (QAOA) and quantum convolutional neural network (QCNN). We demonstrate up to 59% fidelity improvement in simulation by optimizing the heavy-hexagon architecture for QAOA maxcut circuits, and up to 14% improvement on the grid architecture. For the QCNN circuit, architecture optimization improves fidelity by 11% on the heavy-hexagon architecture and 605% on the grid architecture.

关键词： Computer architecture Logic gates Qubit optimization Quantum circuit Layout Hardware Quantum architecture domain-specific architecture architecture optimization design automation

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CHAOS THEORY, ADVANCED METAHEURISTIC ALGORITHMS AND THEIR NEWFANGLED DEEP LEARNING architecture optimization APPLICATIONS: A REVIEW

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2024年第3期32卷

作者： Akgul, Akif Karaca, Yell'z Pala, Muhammed Ali Cimen, Murat Erhan Boz, Ali Fuat Yildiz, Mustafa Zahid Hitit Univ Fac Engn Dept Comp Engn Corum Turkiye Univ Massachusetts Chan Med Sch Worcester MA 01655 USA Sakarya Univ Appl Sci Fac Technol Dept Elect & Elect Engn Sakarya Turkiye

Metaheuristic techniques are capable of representing optimization frames with their specific theories as well as objective functions owing to their being adjustable and effective in various applications. Through the optimization of deep learning models, metaheuristic algorithms inspired by nature, imitating the behavior of living and non-living beings, have been used for about four decades to solve challenging, complex, and chaotic problems. These algorithms can be categorized as evolution-based, swarm-based, nature-based, human-based, hybrid, or chaos-based. Chaos theory, as a useful approach to understanding neural network optimization, has the basic idea of viewing the neural network optimization as a dynamical system in which the equation schemes are utilized from the space pertaining to learnable parameters, namely optimization trajectory, to itself, which enables the description of the evolution of the system by understanding the training behavior, which is to say the number of iterations over time. The examination of the recent studies reveals the importance of chaos theory, which is sensitive to initial conditions with randomness and dynamical properties that are principally emerging on the complex multimodal landscape. Chaotic optimization, in this regard, accelerates the speed of the algorithm while also enhancing the variety of movement patterns. The significance of hybrid algorithms developed through their applications in different domains concerning real-world phenomena and well-known benchmark problems in the literature is also evident. Metaheuristic optimization algorithms have also been applied to deep learning or deep neural networks (DNNs), a branch of machine learning. In this respect, the basic features of deep learning and DNNs and the extensive use of metaheuristic algorithms are overviewed and explained. Accordingly, the current review aims at providing new insights into the studies that deal with metaheuristic algorithms, hybrid-based metaheu

关键词： Metaheuristic Algorithms Chaos-based Metaheuristic Algorithms Evolution-based Metaheuristic Algorithms Swarm-based Metaheuristic Algorithms Nature-based Metaheuristic Algorithms Human-based Metaheuristic Algorithms Hybrid Metaheuristic Algorithms architecture optimization Deep Learning Engineering Applications Natural Mathematics Engineering Mathematics Applicability-based optimization Chaos Theory

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architecture optimization of Hybrid Electric Vehicles with Future High-Efficiency Engine

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ENERGIES 2018年第5期11卷

作者： Hong, Jinlong Zhao, Liangchun Lei, Yulong Gao, Bingzhao Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Jilin Peoples R China

The great development of engine technologies can help to improve the engine characteristics and performance: a better thermal efficiency and an extending fuel economy area, which will subsequently decrease the fuel consumption and thus influence the overall architecture of the vehicle. In this paper, an investigation is carried out to assess the influence of the high-efficiency engine on the transmission gear numbers. First, according to the relevant studies and the integration of the advanced engine technology, a future engine fuel consumption map is obtained, based on which, the preliminary simulations are applied to explore the best match between the transmission and the proposed future engine from the perspective of fuel consumption. The simulation results indicate that the transmission with four gears is the best option to match the future engine while maintaining good fuel economy and meeting the driving demands. Then, based on this conclusion, a new hybrid powertrain architecture, which includes four gears for the engine, is introduced and analyzed in detail, with the advantage of seamless gear shift due to the compensation torque of the motor. Finally, to further examine the fuel economy and gear shift quality of the proposed powertrain, the dynamic model is established and the simulation results demonstrate that the new powertrain architecture shows a good fuel consumption performance and the gear shift process can be achieved without power interruption.

关键词： fuel consumption hybrid electric vehicles architecture optimization gear shift

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Exploring Automotive Stakeholder Requirements for architecture optimization Support

Exploring Automotive Stakeholder Requirements for Architectu...

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IEEE International Conference on Software architecture (ICSA-C)

作者： Waegemann, Tobias Kolagari, Ramin Tavakoli Schmid, Klaus Tech Hsch Nurnberg Dept Comp Sci Nurnberg Germany Univ Hildesheim Dept Comp Sci Hildesheim Germany

ISBN: (纸本)9781728118765

In automotive system design, system architects increasingly use model-based approaches for defining product line architectures (PLAs). This trend is strongly supported by the emergence of domain standards like EAST-ADL and AUTOSAR that facilitate a system design approach based on systematic reuse. In our work, we focus on the automated exploration of PLA design spaces by means of multi-objective optimization and ask what are challenges that stakeholders face as part of this design task. Optimizing PLA design spaces shares certain challenges with conventional architecture optimization, like conflicting quality goals and potentially vast design spaces. However, PLA optimization exacerbates these challenges by an additional layer of complexity in the system architecture, due to product line variability in the system. Our main focus in this paper is on achieving a better understanding of the stakeholder requirements on a support infrastructure for automated PLA design space exploration based on PLA system models. We also address the challenges that stakeholders must overcome when integrating such an approach into an existing process landscape. For that purpose, we conducted a study with automotive industry experts and analyzed the findings using the qualitative data analysis methods of Grounded Theory. Based on the study results, we derive a set of challenges for the realization and integration of automated and semi-automated PLA design space exploration in industrial environments.

关键词： design space exploration software product lines value-oriented design process architecture optimization stakeholder study

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