The recent developments in the research area of optimization and machine learning have shown a significant shift towards more sophisticated and efficient methods for solving complex engineering and computational problems. There is a growing emphasis on integrating advanced machine learning techniques with traditional optimization methods to enhance the accuracy and efficiency of solutions. This trend is particularly evident in the fields of software defect prediction, control system optimization, and material characterization. Innovations such as multi-objective bilevel optimization, Bayesian optimization, and probabilistic reduced-order modeling are being leveraged to address the inherent complexities and uncertainties in these domains. Additionally, the use of high-fidelity data and full-field measurements is becoming more prevalent, enabling more reliable parameter inference and model calibration. The integration of these advanced methods with real-world applications, such as semiconductor manufacturing and cardiovascular modeling, underscores the practical impact and potential for widespread adoption. Notably, the development of novel algorithms that combine evolutionary strategies with local search techniques and opposition-based learning is showing promise in improving the performance of multi-objective optimization problems. These advancements collectively indicate a move towards more adaptive, data-driven, and computationally efficient optimization strategies that can handle high-dimensional and heterogeneous data effectively.
Sophisticated Optimization and Machine Learning Integration
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Feature Importance in the Context of Traditional and Just-In-Time Software Defect Prediction Models
Inversion-based Latent Bayesian Optimization
Improving Computational Cost of Bayesian Optimization for Controller Tuning with a Multi-stage Tuning Framework
What is Metaheuristics? A Primer for the Epidemiologists
Reliability-Based Design Optimization Incorporating Extended Optimal Uncertainty Quantification
Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings
MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning
Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference
Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization
Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
Integrating Chaotic Evolutionary and Local Search Techniques in Decision Space for Enhanced Evolutionary Multi-Objective Optimization
Design optimization of semiconductor manufacturing equipment using a novel multi-fidelity surrogate modeling approach
An Ising Machine Formulation for Design Updates in Topology Optimization of Flow Channels
Evolutionary Algorithm with Detection Region Method for Constrained Multi-Objective Problems with Binary Constraints
A probabilistic reduced-order modeling framework for patient-specific cardio-mechanical analysis
Retrieval of sun-induced plant fluorescence in the O$_2$-A absorption band from DESIS imagery
Developement of Reinforcement Learning based Optimisation Method for Side-Sill Design
Data-driven parameterization refinement for the structural optimization of cruise ship hulls