CMA-ES is an advanced optimization algorithm used in evolutionary computation, particularly for optimizing real-valued multidimensional functions. It adapts the covariance matrix of a multivariate normal distribution to efficiently explore the search space, allowing for effective convergence towards optimal solutions. This technique is especially beneficial when dealing with complex landscapes often encountered in evolving neural network topologies.
congrats on reading the definition of CMA-ES (Covariance Matrix Adaptation Evolution Strategy). now let's actually learn it.