Learning bayesian networks by ant colony optimisation: searching in two different spaces

Resum

The most common way of automatically learning Bayesian networks
from data is the combination of a scoring metric, the evaluation of the
fitness of any given candidate network to the data base, and a
search procedure to explore the search space. Usually, the search
is carried out by greedy hill-climbing algorithms, although other techniques
such as genetic algorithms, have also been used.

A recent metaheuristic, Ant Colony Optimisation (ACO), has
been successfully applied to solve a great variety of problems,
being remarkable the performance achieved in
those problems related to path (permutation) searching in
graphs, such as the Traveling Salesman Problem.
In two previous works [13,12], the authors have approached the problem of learning
Bayesian networks by means of the search+score methodology using
ACO as the search engine.

As in these articles the search was performed in different search spaces,
in the space of orderings [13] and in the space of directed acyclic graphs [12].
In this paper we compare both approaches
by analysing the results obtained and the differences in the design and
implementation of both algorithms.