Machine Learning is one the most promising and salient research area in artificial intelligence, which has experienced a rapid development and has become a powerful tool in a wide range of applications. In studies relevant to both evolutionary algorithms and machine learning techniques, many attempts have been made to apply variants evolutionary algorithms as types of effective and efficient machine learning techniques. In contrast with the view of using evolutionary algorithms as machine learning techniques, we have focused on using machine learning techniques to enhance evolutionary algorithms. In the framework of enhanced evolutionary algorithms with machine learning techniques, the main idea is that the evolutionary algorithm has stored ample data about the search space, problem features and population information during the iterative search process, thus the machine learning technique is helpful in analyzing these data for enhancing the search performance. In this way, useful information can be extracted to understand the search behavior and to assist with future searches for the global optimum. In many applications, evolutionary algorithms incorporating machine learning techniques have been proven to be advantageous in both convergence speed and solution quality. The CMA-ES algorithm is one of the evolutionary algorithms that produce new population by sampling from a normal distribution. Covariance Matrix is one of the normal distribution parameters that is updated in each generation and has an important role in population quality and consequently guidance of the evolution. Therefore how updating this covariance matrix is critical. On the other hand, the DCC-GARCH model is one of the machine learning techniques that have several applications in economic models. This model predict multivariate time series which each term of the series is sampled from a normal distribution with zero mean and covariance matrix H t . Instead of predicting next term directly, this model predict the covariance matrix of the next term such that sampling from this covariance matrix may produce next term of time series. In this thesis, we want to use DCC-GARCH model to enhance the CMA-ES algorithm. The best step in each generation is equal to a term of series and the model will predict the covariance matrix of the best step of next generation by this series. Then this matrix is used in updating the covariance matrix of the CMA-ES algorithm directly and in directly that the results and experiments is showing reasonable enhancement.
