Building multivariate density functions based on promising direction vectors
Ignacio Segovia-Domínguez, Arturo Hernández Aguirre · 2013
In this paper we introduce a method to build a large variety of multivariate density functions based on univariate distributions and promising direction vectors. The stochastic model constructed in our proposal simulates random vector towards directions with high probability of improving the population. Also, we provide two algorithms to use this ideas in the global optimization problem. The first one is a Hybrid Estimation of Distribution Algorithm and the second one is the Adaptive Basis of Evolution Strategy. Both algorithms are tested and show a good performance in a set of benchmark problems, even outperforming popular competitive algorithms. In the best of our knowledge, the central idea described here is not in previous literature about global optimization.