All Stories

  1. A survey of features used for representing black-box single-objective continuous optimization
  2. Behaviour Space Analysis of LLM-Driven Meta-Heuristic Discovery
  3. Mechanistic Interpretability for Transformer-Based Time Series Classification
  4. Benchmarking that Matters: Rethinking Benchmarking in Continuous Optimisation for Practical Impact
  5. From Performance to Understanding: A Vision for Explainable Automated Algorithm Design
  6. Investigating the Interplay of Parameterization and Optimizer in Gradient-Free Topology Optimization: A Cantilever Beam Case Study
  7. LLM Driven Design of Continuous Optimization Problems with Controllable High-Level Properties
  8. Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
  9. Agentic Large Language Models, a Survey
  10. Latest Approaches in Reasoning Large Language Models
  11. Gradient Free Multi-Objective Counterfactual Explainability for Multivariate Time Series Classification
  12. REMAINING USEFUL LIFE IN COMPLEX MULTI-COMPONENT SYSTEMS: TAXONOMY, REVIEW, AND RESEARCH DIRECTIONS
  13. EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
  14. Quality–diversity-driven robust evolutionary optimization of optical designs
  15. Development of a photonic crystal spectrometer for greenhouse gas measurements
  16. LLaMEA: Automatically Generating Metaheuristics with Large Language Models
  17. BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics
  18. Explainable Benchmarking for Iterative Optimization Heuristics
  19. Neighborhood Adaptive Differential Evolution
  20. Structural bias in optimization algorithms
  21. Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery
  22. Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms
  23. Why Are You Wrong? Counterfactual Explanations for Language Grounding with 3D Objects
  24. Explainable Benchmarking for Iterative Optimization Heuristics
  25. In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
  26. LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics
  27. Surrogate-based automated hyperparameter optimization for expensive automotive crashworthiness optimization
  28. Stalling in Space: Attractor Analysis for Any Algorithm
  29. Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms
  30. XAI for Benchmarking Black-Box Metaheuristics
  31. Explainable AI for Evolutionary Computation
  32. An Introduction to the Crossroads of Explainable Artificial Intelligence and Evolutionary Computation
  33. Global Sensitivity Analysis Is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design
  34. Leveraging Lightweight Generators for Memory Efficient Continual Learning
  35. ACO-NSGAII: A Novel Metaheuristics for Bi-Objective Electric Vehicle Routing Problems
  36. A Functional Analysis Approach to Symbolic Regression
  37. A Corridor Model Evolutionary Algorithm for Fast Converging Green Vehicle Routing Problem
  38. A Critical Analysis of Raven Roost Optimization
  39. Modular Optimization Framework for Mixed Expensive and Inexpensive Real-World Problems
  40. Hot off the Press: Parallel Multi-Objective Optimization for Expensive and Inexpensive Objectives and Constraints
  41. Landscape Analysis Based vs. Domain-Specific Optimization for Engineering Design Applications: A Clear Case
  42. Quality-diversity driven robust evolutionary optimization of optical designs
  43. Parallel multi-objective optimization for expensive and inexpensive objectives and constraints
  44. Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization
  45. A Deep Dive Into Effects of Structural Bias on CMA-ES Performance Along Affine Trajectories
  46. Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems
  47. TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
  48. Towards Fairness in Machine Learning: Balancing Racially Imbalanced Datasets Through Data Augmentation and Generative AI
  49. Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI
  50. Landscape-Aware Automated Algorithm Configuration Using Multi-output Mixed Regression and Classification
  51. Clustering-based Domain-Incremental Learning
  52. Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations
  53. New solutions to Cooke triplet problem via analysis of attraction basins
  54. DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
  55. Deep BIAS: Detecting Structural Bias using Explainable AI
  56. AI for Expensive Optimization Problems in Industry
  57. Application of Functional Kernel Hypothesis Testing for Channel Selection in Time Series Classification
  58. Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection
  59. BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances
  60. Challenges of ELA-Guided Function Evolution Using Genetic Programming
  61. A data-centric approach to anomaly detection in layer-based additive manufacturing
  62. Evolutionary Algorithms for Parameter Optimization—Thirty Years Later
  63. The Opaque Nature of Intelligence and the Pursuit of Explainable AI
  64. Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
  65. BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain
  66. GSAreport: Easy to Use Global Sensitivity Reporting
  67. A Comparison of Global Sensitivity Analysis Methods for Explainable AI with an Application in Genomic Prediction
  68. A Comparison of Global Sensitivity Analysis Methods for Explainable AI with an Application in Genomic Prediction
  69. Analysis of how bias in search algorithms can affect the search behaviour
  70. Exploratory Landscape Analysis in Real-World Engineering Optimization
  71. Multi-point acquisition function for constraint parallel efficient multi-objective optimization
  72. End-to-End Pipeline for Uncertainty Quantification and Remaining Useful Life Estimation: An Application on Aircraft Engines
  73. Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines
  74. Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations
  75. Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation
  76. Optimally Weighted Ensembles for Efficient Multi-objective Optimization
  77. Analysis of Structural Bias in Differential Evolution Configurations
  78. A Comparison of many Global Sensitivity Analysis methods
  79. Exploiting Generative Models for Performance Predictions of 3D Car Designs
  80. Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
  81. Point2FFD: Learning Shape Representations of Simulation-Ready 3D Models for Engineering Design Optimization
  82. BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain
  83. BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain
  84. Emergence of structural bias in differential evolution
  85. Exploiting Local Geometric Features in Vehicle Design Optimization with 3D Point Cloud Autoencoders
  86. SAMO-COBRA: A Fast Surrogate Assisted Constrained Multi-objective Optimization Algorithm
  87. Requirements towards optimizing analytics in industrial processes
  88. A Tailored NSGA-III for Multi-objective Flexible Job Shop Scheduling
  89. Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds
  90. Neural Network Design: Learning from Neural Architecture Search
  91. Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search
  92. Feature Visualization for 3D Point Cloud Autoencoders
  93. A New Approach Towards the Combined Algorithm Selection and Hyper-parameter Optimization Problem
  94. On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization
  95. Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders
  96. Cluster-based Kriging approximation algorithms for complexity reduction
  97. Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization
  98. Designing Ships Using Constrained Multi-objective Efficient Global Optimization
  99. A Novel Uncertainty Quantification Method for Efficient Global Optimization
  100. A multi-method simulation of a high-frequency bus line
  101. A new acquisition function for Bayesian optimization based on the moment-generating function
  102. Algorithm configuration data mining for CMA evolution strategies
  103. Time complexity reduction in efficient global optimization using cluster kriging
  104. A framework for evaluating meta-models for simulation-based optimisation
  105. Local subspace-based outlier detection using global neighbourhoods
  106. Towards Data Driven Process Control in Manufacturing Car Body Parts
  107. Fuzzy clustering for Optimally Weighted Cluster Kriging
  108. An Incremental Algorithm for Repairing Training Sets with Missing Values
  109. Analysis and Visualization of Missing Value Patterns
  110. Optimally Weighted Cluster Kriging for Big Data Regression
  111. Fitness Landscape Analysis of NK Landscapes and Vehicle Routing Problems by Expanded Barrier Trees