Implements the Estimation of Multivariate Normal Algorithm (EMNA), an Estimation of Distribution Algorithm (EDA) for continuous optimization problems. The method iteratively selects the best individuals from a population, estimates the multivariate normal distribution of these selected individuals, and generates new candidate solutions by sampling from the estimated distribution. This process allows the probability model to adapt toward promising regions of the search space. The implementation includes an option to slow down convergence, along with an optional explicit exploration strategy for population initialization. This explicit exploration strategy is described in Salinas-Gutierrez and Muñoz-Zavala (2023)