On the Utility of Linear Transformations for Population-Based Optimization Algorithms
Abstract
Many population-based real-valued optimization algorithms assume statistical independence of individual parts of solution. This assumption is only seldom fulfilled. In real domain, some coordinate transformations can be applied to reduce the dependency among variables which makes the optimization problem easier to solve. This article reviews two common linear transformations, principal and independent component analysis (PCA, ICA). Although ICA should work for our purposes better, it is shown that there are cases when PCA results in a better performance.
