Mastersthesis,
Selective Crossover Using Gene Dominance as an Adaptive Strategy for Genetic Programming
University College, London, UK, MSc Intelligent Systems, (September 2004)
Abstract
Since the emergence of the evolutionary computing,
many new natural genetic operators have been research
and within genetic algorithms and many new
recombination techniques have been proposed. There has
been substantially less development in Genetic
Programming compared with Genetic Algorithms. Koza
koza:gp2 stated that crossover was much more
influential than mutation for evolution in genetic
programming; suggesting that mutation was unnecessary.
A well known problem with crossover is that good
sub-trees can be destroyed by an inappropriate choice
of crossover point. This is otherwise known as
destructive crossover.
This thesis proposes two new crossover methods which
uses the idea of haploid gene dominance in genetic
programming. The dominance information identifies the
goodness of a particular node, or the sub-tree, and aid
to reduce destructive crossover. The new selective
crossover techniques will be used to test a variety of
optimisation problems and compared with the analysis
work by Vekaria 28. Additionally, uniform crossover
which Poli and Langdon 22 proposed has been revised
and discussed.
The gene dominance selective crossover operator was
initially designed by Vekaria in 1999 who implemented
it for Genetic Algorithms and showed improvement in
performance when evaluated on certain problems. The
proposed operators, "Simple Selective Crossover"
and "Dominance Selective Crossover", have been
compared and contrasted with Vekaria results on two
problems; an attempt has also been made to test it on a
more complex genetic programming problem. Satisfactory
results have been found.
