  Over the past decades, more and more methods gain a giant development due to
the development of technology. Evolutionary Algorithms are widely used as a
heuristic method. However, the budget of computation increases exponentially
when the dimensions increase. In this paper, we will use the dimensionality
reduction method Principal component analysis (PCA) to reduce the dimension
during the iteration of Covariance Matrix Adaptation Evolution Strategy
(CMA-ES), which is a good Evolutionary Algorithm that is presented as the
numeric type and useful for different kinds of problems. We assess the
performance of our new methods in terms of convergence rate on multi-modal
problems from the Black-Box Optimization Benchmarking (BBOB) problem set and we
also use the framework COmparing Continuous Optimizers (COCO) to see how the
new method going and compare it to the other algorithms.
