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CMA-ES is one of the state-of-the art evolutionary algorithms. It consists of sampling from multivariate normal distribution, whose covariance matrix is claimed to approximate the inverse hessian of the objective function. The midpoint of this distribution should be therefore the best linear unbiased estimator of the optimum. This hypothesis was tested on the BBOB 2013 benchmark set using the standard CMA-ES implementation. Evaluation of the objective function in the midpoint neither improves nor deteriorates the performance of the algorithm. Moreover, it turns out that the standard implementation of CMA-ES is competitive but not as good as the best CMA-ES variants, which took parts in the BBOB 2009 competition.
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Projects co-financed by:
Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.
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