Publication at Faculty of Mathematics and Physics |
2012
Abstract
The paper presents a surrogate-based evolutionary strategy for multiobjective optimization. The evolutionary strategy uses distance based aggregate surrogate models in two ways as a part of memetic search and as way to pre-select individuals in order to avoid evaluation of bad individuals.
The model predict the distance of individuals to the currently known Pareto set. The newly proposed algorithm is compared to other algorithms which use similar surrogate models on a set of benchmark functions.