BCMA-ES: A Bayesian approach to CMA-ES

In a nutshell, the (µ / λ) CMA-ES is an iterative black box optimization algorithm, that, in each of its iterations, samples λ candidate This paper introduces a novel theoretically sound approach for solutions from a multivariate normal distribution, evaluates the celebrated CMA-ES algorithm. Assuming the parameters of these solutions (sequentially or in parallel) retains µ candidates the multi variate normal distribution for the minimum follow a and adjusts the sampling distribution used for the next iteration conjugate prior distribution, we derive their optimal update at to give higher probability to good samples. Each iteration can be each iteration step. Not only provides this Bayesian framework a individually seen as taking an initial guess or prior for the multi justification for the update of the CMA-ES algorithm but it also gives variate parameters, namely the mean and the covariance, and after two new versions of CMA-ES either assuming normal-Wishart or making an experiment by evaluating these sample points with the normal-Inverse Wishart priors, depending whether we parametrize fit function updating the initial parameters accordingly.