Supervisors:
Anne Auger,
Nikolaus Hansen
Context
Evolution strategies are search or optimization methods that search for good solutions in continuous domain search spaces, where good is defined by a given fitness function. Originally inspired by biological evolution more than forty years ago, evolution strategies have matured and become comparatively well-understood. The covariance matrix adaptation evolution strategy (CMA-ES) is a modern variant that samples from a multivariate normal distribution adapting variances and covariances of the distribution. Many interesting interpretations and justifications of the CMA-ES have been proposed. Most recently, it was shown to conduct a natural gradient descent in the distribution space (as opposed to the search space). We propose several topics connected the CMA-ES.