Benchmarking SHADE algorithm enhanced with model based optimization on the BBOB noiseless testbed

Autor:	Mateusz Zaborski, Michał Okulewicz
Rok vydání:	2021
Předmět:	education.field_of_study Computer science Population Testbed Benchmarking education Algorithm Square (algebra) ComputingMethodologies_COMPUTERGRAPHICS
Zdroj:	GECCO Companion
DOI:	10.1145/3449726.3463290
Popis:	In this paper we evaluate the SHADE-LM algorithm on the BBOB noiseless testbed. The algorithm hybridizes the SHADE algorithm with a model based optimization. This hybridization is performed in a transparent manner for both optimizers, with SHADE having access to the samples provided by model based optimization, and models of square functions are fitted on the current population. The paper compares this extended version with the performance of the version of SHADE by Tanabe and Fukunaga.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0dea1cb9e528713fdcca9169957c71e2 https://doi.org/10.1145/3449726.3463290 Zobrazit plný text záznamu