Enhance CMA-ES

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is an evolutionary strategy which updates the covariance matrix of a normal search distribution. It is used for the optimization of non-linear and non-convex functions. The aim of this project is to implement the following algorithms which enhance the performance of (vanilla/plain) CMA-ES: Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy (saACM-ES), a Restart CMA Evolution Strategy with increasing population size (IPOP-CMA-ES), and Active Covariance Matrix Adaptation (Active-CMA-ES). The API design of each should match that of the rest of the code base. In addition to these, appropriate tests and documentation shall be implemented.