Title: Evolutionary Tree Genetic Programming
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 natural evolution follows a tree-like pattern. Our goal is to simulate similar behavior 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: the Artificial Ant problem and the Multiplexer problem. The performance of the ETGP algorithm is compared to that of GP system. Code size and variance are reduced by a robust but insignificant perceentage in both problems, but 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 behavior 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.