Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions

Abstract

NM-landscapes have been recently introduced as a class of tunable rugged models. They are a subset of the general interaction models where all the interactions are of order less or equal $M$. The Boltzmann distribution has been extensively applied in single-objective evolutionary algorithms to implement selection and study the theoretical properties of model-building algorithms. In this paper we propose the combination of the multi-objective NM-landscape model and the Boltzmann distribution to obtain Pareto-front approximations. We investigate the joint effect of the parameters of the NM-landscapes and the probabilistic factorizations in the shape of the Pareto front approximations.

Accepted for CAEPIA-2015 conference, Albacete, Spain. 11 pages, 3 figures

References in corpus (2)

• A probabilistic evolutionary optimization approach to compute quasiparticle braids
• Tunably Rugged Landscapes with Known Maximum and Minimum