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Found 6 papers, 0 papers with code
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Our proposed method creates algorithm behavior meta-representations, constructs a graph from a set of algorithms based on their meta-representation similarity, and applies a graph algorithm to select a final portfolio of diverse, representative, and non-redundant algorithms.
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.
RF+clust for Leave-One-Problem-Out Performance Prediction
Per-instance automated algorithm configuration and selection are gaining significant moments in evolutionary computation in recent years.
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc.
