Year: 2023
Type: conference-paper
Abstract: The highly multimodal Rastrigin test function is analyzed by deriving a new aggregated progress rate measure. It is derived as a function of the residual distance to the optimizer by assuming normally distributed positional coordinates around the global optimizer. This assumption is justified for successful ES-runs operating with sufficiently slow step-size adaptation. The measure enables the investigation of further convergence properties. For moderately large mutation strengths a characteristic distance-dependent Rastrigin noise floor is derived. For small mutation strengths local attraction is analyzed and an escape condition is established. Both mutation strength regimes combined pose a major challenge optimizing the Rastrigin function, which can be counteracted by increasing the population size. Hence, a population scaling relation to achieve high global convergence rates is derived which shows good agreement with experimental data.
Authors: Amir Omeradzic, Hans-Georg Beyer
Institution: Vorarlberg University of Applied Sciences
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Austrian Science Fund
OpenAlex ID: W4385438216