Abstract
gradient descent search in tree based genetic
programming for object recognition problems. To learn
better partial programs, a weight parameter is
introduced in each link between every two nodes in a
program tree, so that a change of a weight corresponds
to a change of the effect of the sub-program tree.
Inside a particular generation, weight changes are
learnt locally by gradient descent search, but the
whole evolution process is still carried out across
different generations globally by the genetic beam
search. This approach is examined and compared with the
basic genetic programming approach without gradient
descent on three object classification problems of
various difficulty. The results suggest that the new
approach outperforms the basic approach on all
problems.
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