keywords = "genetic algorithms, genetic programming, belief
networks, probability, trees (mathematics)Bayesian
network, conditional probability table, evolutionary
algorithms, expanded parse tree, powerful optimization
algorithm, probabilistic techniques, program
generation",
ISSN = "1089-778X",
DOI = "doi:10.1109/TEVC.2008.915999",
size = "15 pages",
abstract = "Genetic programming (GP) is a powerful optimization
algorithm that has been applied to a variety of
problems. This algorithm can, however, suffer from
problems arising from the fact that a crossover, which
is a main genetic operator in GP, randomly selects
crossover points, and so building blocks may be
destroyed by the action of this operator. In recent
years, evolutionary algorithms based on probabilistic
techniques have been proposed in order to overcome this
problem. In the present study, we propose a new program
evolution algorithm employing a Bayesian network for
generating new individuals. It employs a special
chromosome called the expanded parse tree , which
significantly reduces the size of the conditional
probability table (CPT). Prior prototype tree-based
approaches have been faced with the problem of huge
CPTs, which not only require significant memory
resources, but also many samples in order to construct
the Bayesian network. By applying the present approach
to three distinct computational experiments, the
effectiveness of this new approach for dealing with
deceptive problems is demonstrated.",
notes = "POLE, EPT, Kullback-Leibler. Max problem
\cite{langdon:1997:MAX}. DMAX deceptive max problem.
Royal tree problem.