Dimensionality reduction in continuous evolutionary optimization

Dimensionality reduction methods compute a mapping from a high-dimensional space to a space with lower dimensions while preserving important information. The idea of hybridizing dimensionality reduction with evolution strategies is that the search in a space that employs a larger dimensionality than the original solution space may be easier. We propose a dimensionality reduction evolution strategy (DRES) based on a self-adaptive (μ, λ)-ES that generates points in a space with a dimensionality higher than the original solution space. After the population has been generated, it is mapped to the solution space with dimensionality reduction (DR) methods, the solutions are evaluated and the best w.r.t. the fitness in the original space are inherited to the next generation. We employ principal component analysis (PCA) as DR method and show a performance tweak on a small set of benchmark problems.