A Self-Tuning Mechanism for Depth-Dependent
Crossover
Advances in Genetic Programming 3, chapter 16, MIT Press, Cambridge, MA, USA, (June 1999)Abstract
There are three genetic operators: crossover, mutation
and reproduction in Genetic Programming (GP). Among
these genetic operators, the crossover operator mainly
contributes to searching for a solution program.
Therefore, we aim at improving the program generation
by extending the crossover operator. The normal
crossover selects crossover points randomly and
destroys building blocks. We think that building blocks
can be protected by swapping larger substructures. In
our former work, we proposed a depth-dependent
crossover. The depth-dependent crossover protected
building blocks and constructed larger building blocks
easily by swapping shallower nodes. However, there was
problem-dependent characteristics on the
depth-dependent crossover, because the depth selection
probability was fixed for all nodes in a tree. To solve
this difficulty, we propose a self-tuning mechanism for
the depth selection probability. We call this type of
crossover a "self-tuning depth-dependent
crossover". We compare GP performances of the
selftuning depthdependent crossover with performances
of the original depth-dependent crossover. Our
experimental results clarify the superiority of the
self tuning depth dependent crossover.