Modeling dependencies between decision variables and objectives with copula models
Abdelhakim Cheriet, Roberto Santana · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
Probabilistic modeling in multi-objective optimization problems (MOPs) has mainly focused on capturing and representing the dependencies between decision variables in a set of selected solutions. Recently, some works have proposed to model also the dependencies between the objective variables, which are represented as random variables, and the decision variables. In this paper, we investigate the suitability of copula models to capture and exploit these dependencies in MOPs with a continuous representation. Copulas are very flexible probabilistic models able to represent a large variety of probability distributions.