An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions
Over the recent years, continuous optimization has significantly evolved to become the
mature research field it is nowadays. Through this process, evolutionary algorithms had an …
mature research field it is nowadays. Through this process, evolutionary algorithms had an …
Automated algorithm selection: Survey and perspectives
It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …
intensely studied, different instances are best solved using different algorithms. This is …
CMA-ES for hyperparameter optimization of deep neural networks
I Loshchilov, F Hutter - arXiv preprint arXiv:1604.07269, 2016 - arxiv.org
Hyperparameters of deep neural networks are often optimized by grid search, random
search or Bayesian optimization. As an alternative, we propose to use the Covariance Matrix …
search or Bayesian optimization. As an alternative, we propose to use the Covariance Matrix …
Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning
P Kerschke, H Trautmann - Evolutionary computation, 2019 - direct.mit.edu
In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Combining latent space and structured kernels for Bayesian optimization over combinatorial spaces
We consider the problem of optimizing combinatorial spaces (eg, sequences, trees, and
graphs) using expensive black-box function evaluations. For example, optimizing molecules …
graphs) using expensive black-box function evaluations. For example, optimizing molecules …
Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model
An appropriate yaw angle misalignment of the wind turbines in a wind farm has been found
to improve the average energy production of the turbine array. Predicting the spatial …
to improve the average energy production of the turbine array. Predicting the spatial …
[HTML][HTML] Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models
Hyperparameter tuning is a critical function necessary for the effective deployment of most
machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of …
machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of …
Evolutionary algorithms for parameter optimization—thirty years later
Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …
developments in the field of evolutionary algorithms, with applications in parameter …
Warm starting CMA-ES for hyperparameter optimization
Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is
recognized as essential for automation and high performance of machine learning …
recognized as essential for automation and high performance of machine learning …
Evolutionary computation for wind farm layout optimization
This paper presents the results of the second edition of the Wind Farm Layout Optimization
Competition, which was held at the 22nd Genetic and Evolutionary Computation …
Competition, which was held at the 22nd Genetic and Evolutionary Computation …