The importance of landscape features for performance prediction of modular CMA-ES variants
Ana Kostovska, Diederick Vermetten, Sašo Džeroski, Carola Doerr, Peter Korošec, Tome Eftimov
2022Proceedings of the Genetic and Evolutionary Computation Conference15 citationsDOIOpen Access PDF
Abstract
Selecting the most suitable algorithm and determining its hyperparameters for a given optimization problem is a challenging task. Accurately predicting how well a certain algorithm could solve the problem is hence desirable. Recent studies in single-objective numerical optimization show that supervised machine learning methods can predict algorithm performance using landscape features extracted from the problem instances.
Topics & Concepts
Computer scienceHyperparameterTask (project management)Machine learningModular designArtificial intelligenceEngineeringOperating systemSystems engineeringAdvanced Multi-Objective Optimization AlgorithmsMetaheuristic Optimization Algorithms ResearchEnergy Load and Power Forecasting