Combinatorial Optimization by Learning and Simulation of Bayesian Networks

TL;DR

This paper introduces a novel approach that integrates Bayesian networks into Estimation of Distribution Algorithms to enhance combinatorial optimization, demonstrating promising experimental results.

Contribution

It presents new methods combining structure learning and simulation of Bayesian networks within EDAs for improved combinatorial optimization.

Findings

Effective integration of Bayesian networks into EDAs.

Improved optimization performance demonstrated in experiments.

Novel probabilistic graphical model approaches for combinatorial problems.

Abstract

This paper shows how the Bayesian network paradigm can be used in order to solve combinatorial optimization problems. To do it some methods of structure learning from data and simulation of Bayesian networks are inserted inside Estimation of Distribution Algorithms (EDA). EDA are a new tool for evolutionary computation in which populations of individuals are created by estimation and simulation of the joint probability distribution of the selected individuals. We propose new approaches to EDA for combinatorial optimization based on the theory of probabilistic graphical models. Experimental results are also presented.