abstract = "We introduce a clustering-based method of
subpopulation management in genetic programming (GP)
called Evolutionary Tree Genetic Programming (ETGP).
The biological motivation behind this work is the
observation that the natural evolution follows a
tree-like phylogenetic pattern. Our goal is to simulate
similar behavior in artificial evolutionary systems
such as GP. To test our model we use three common GP
benchmarks: the Ant Algorithm, 11-Multiplexer, and
Parity problems.The performance of the ETGP system is
empirically compared to those of the GP system. Code
size and variance are consistently reduced by a small
but statistically significant percentage, resulting in
a slight speedup in the Ant and 11-Multiplexer
problems, while the same comparisons on the Parity
problem are inconclusive.",
notes = "GECCO-2005 A joint meeting of the fourteenth
international conference on genetic algorithms
(ICGA-2005) and the tenth annual genetic programming
conference (GP-2005).