Mastersthesis,
Evolutionary Tree Genetic Programming
Department of Computing and Information Sciences, College of Arts and Sciences, Kansan State University, Manhattan, Kansas, USA, Master of Science, (2004)
Abstract
We introduce an extension of a genetic programming
(GP) algorithm we call Evolutionary Tree Genetic
Programming (ETGP). The biological motivation behind
this work is the observation that the natural evolution
follows a tree like pattern. We want to simulate
similar behaviour in artificial evolutionary systems
such as GP. In this thesis we provide multiple reasons
why we believe simulation of this phenomenon can be
beneficial for GP systems. We present various empirical
results from test runs. As the test bed for our
experiments two standard benchmark problems for GP
systems are used, particularly the Artificial Ant
problem and the Multiplexer problem. The performance of
the ETGP algorithm is compared to the performance of GP
system. Unfortunately no significant speedup is found.
Some unexpected behaviors of our system are also
identified, and a hypothesis is formulated that
addresses the question of why we observe this strange
behaviour and the lack of speedup. Suggestions on how
to extend the ETGP system to overcome the problems
identified by this hypothesis are then presented in the
end of our concluding chapter.
