Toward a Matrix-Free Covariance Matrix Adaptation Evolution Strategy

Jarosław Arabas , Dariusz Jagodziński


In this paper, we discuss a method for generating new individuals such that their mean vector and the covariance matrix are defined by formulas analogous to the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In contrast to CMA-ES, which generates new individuals using multivariate Gaussian distribution with an explicitly defined covariance matrix, the introduced method uses combinations of difference vectors between archived individuals and univariate Gaussian random vectors along directions of past shifts of the population midpoints. We use this method to formulate the Differential Evolution Strategy (DES) – an algorithm that is a crossover between Differential Evolution (DE) and CMA-ES. The numerical results presented in the paper indicate that DES is competitive against CMA-ES in performing both local and global optimization.
Author Jarosław Arabas (FEIT / IN)
Jarosław Arabas,,
- The Institute of Computer Science
, Dariusz Jagodziński (FEIT / IN)
Dariusz Jagodziński,,
- The Institute of Computer Science
Journal seriesIEEE Transactions on Evolutionary Computation, ISSN 1089-778X, e-ISSN 1941-0026
Issue year2020
Publication size in sheets0.7
Keywords in EnglishCovariance matrices , Sociology , Optimization , History , Gaussian distribution , Indexes
ASJC Classification1703 Computational Theory and Mathematics; 1712 Software; 2614 Theoretical Computer Science
ProjectDevelopment of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Arabas Jarosław, , Phone: +48 22 234 7432, start date 01-08-2018, end date 30-09-2019, II/2018/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
Score (nominal)200
Score sourcejournalList
ScoreMinisterial score = 200.0, 04-09-2020, ArticleFromJournal
Publication indicators Scopus Citations = 1; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 4.854; WoS Impact Factor: 2018 = 8.508 (2) - 2018=10.364 (5)
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