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Found 11 papers, 3 papers with code
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features.
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
In black-box optimization, it is essential to understand why an algorithm instance works on a set of problem instances while failing on others and provide explanations of its behavior.
Assessing the Generalizability of a Performance Predictive Model
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model.
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction
That is, instead of considering cosine distance in the feature space, we consider a weighted distance measure, with weights depending on the relevance of the feature for the regression model.
SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison
Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets that are non-redundant and are representative of typical optimization scenarios.
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
In addition, we have shown that by using classifiers that take the features relevance on the model accuracy, we are able to predict the status of individual modules in the CMA-ES configurations.
Explainable Landscape Analysis in Automated Algorithm Performance Prediction
Predicting the performance of an optimization algorithm on a new problem instance is crucial in order to select the most appropriate algorithm for solving that problem instance.
Explainable Landscape-Aware Optimization Performance Prediction
In this study, we are investigating explainable landscape-aware regression models where the contribution of each landscape feature to the prediction of the optimization algorithm performance is estimated on a global and local level.
Less is more: Selecting the right benchmarking set of data for time series classification
By observing discrepancy between the empirical results of the bootstrap evaluation and recently adapted practices in TSC literature when introducing novel methods we warn on the potentially harmful effects of tuning the methods on certain parts of the landscape (unless this is an explicit and desired goal of the study).
A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems
When designing a benchmark problem set, it is important to create a set of benchmark problems that are a good generalization of the set of all possible problems.
Personalizing Performance Regression Models to Black-Box Optimization Problems
Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configuration techniques.
