abstract = "In spite of many success stories in various domains,
Genetic Algorithm and Genetic Programming still suffer
from some significant pitfalls. Those evolved programs
often lack of some important properties such as
robustness, comprehensibility, transparency,
modifiability and usability of domain knowledge easily
available. We attempt to resolve these problems, at
least in evolving high-level behaviours, by adopting a
technique of conditions-and-behaviours originally used
for minimizing the learning space in reinforcement
learning. We experimentally validate the approach on a
foraging task.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference
(GP-99).