@inbook {IOPORT.05820962,
    author       = {Omidvar, Mohammad Nabi and Li, Xiaodong},
    title        = {A comparative study of CMA-ES on large scale global optimisation.},
    year         = {2010},
    booktitle    = {AI 2010: Advances in artificial intelligence. 23rd Australasian joint conference, Adelaide, Australia, December 7--10, 2010. Proceedings},
    isbn         = {978-3-642-17431-5},
    pages        = {303-312},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-17432-2_31},
    abstract     = {Summary: In this paper, we investigate the performance of CMA-ES on large scale non-separable optimisation problems. CMA-ES is a robust local optimiser that has shown great performance on small-scale non-separable optimisation problems. Self-adaptation of a covariance matrix makes it rotational invariant which is a desirable property, especially for solving non-separable problems. The focus of this paper is to compare the performance of CMA-ES with Cooperative Co-evolutionary Algorithms (CCEAs) for large scale global optimisation (on problems with up to 1000 real-valued variables). Since the original CMA-ES is incapable of handling problems with more than several hundreds dimensions, sep-CMA-ES was developed using only the diagonal elements of the covariance matrix. In this paper sep-CMA-ES is compared with several existing CCEAs. Experimental results revealed that the performance of sep-CMA-ES drops significantly when the dimensionality of the problem increases. However, our results suggest that the rotational invariant property of CMA-ES can be utilised in conjunction with a CCEA to further enhance its capability to handle large scale optimisation problems.},
    identifier   = {05820962},
}