Towards a Matrix-free Covariance Matrix Adaptation Evolution Strategy
Jarosław Arabas , Dariusz Jagodziński
AbstractIn 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.
|Journal series||IEEE Transactions On Evolutionary Computation, ISSN 1089-778X, (A 50 pkt)|
|Publication size in sheets||0.3|
|Keywords in English||Covariance matrices , Sociology , Optimization , History , Gaussian distribution , Indexes|
|ASJC Classification||; ;|
|project||Development 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, planned end date 30-09-2019, II/2018/DS/1, Implemented
|Score||= 50.0, 04-09-2019, ArticleFromJournal|
|Publication indicators||: 2017 = 4.724; : 2017 = 8.124 (2) - 2017=8.481 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.