abstract = "We consider multi-task learning of visual concepts
within genetic programming (GP) framework. The proposed
method evolves a population of GP individuals, with
each of them composed of several GP trees that process
visual primitives derived from input images. The two
main trees are delegated to solving two different
visual tasks and are allowed to share knowledge with
each other by calling the remaining GP trees
(sub-functions) included in the same individual. The
method is applied to the visual learning task of
recognising simple shapes, using generative approach
based on visual primitives, introduced in [17]. We
compare this approach to a reference method devoid of
knowledge sharing, and conclude that in the worst case
cross-task learning performs equally well, and in many
cases it leads to significant performance improvements
in one or both solved tasks.",
notes = "GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).