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Found 13 papers, 6 papers with code
Deep-BIAS: Detecting Structural Bias using Explainable AI
Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms.
DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e. g., automated selection of optimization algorithms.
Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems.
BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances
Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design.
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes
Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space.
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines
Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation.
Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.
Emergence of Structural Bias in Differential Evolution
Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved.
Neural Network Design: Learning from Neural Architecture Search
Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests.
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling
A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP).
Automatic Configuration of Deep Neural Networks with EGO
In this paper an Efficient Global Optimization (EGO) algorithm is adapted to automatically optimize and configure convolutional neural network architectures.
Cluster-based Kriging Approximation Algorithms for Complexity Reduction
In addition, four Kriging approximation algorithms are proposed as candidate algorithms within the new framework.
Local Subspace-Based Outlier Detection using Global Neighbourhoods
In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often a mixture distribution of multiple components.
