Evolutionary tree genetic programming

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. Categories and Subject Descriptors: I.2.3 [Computing Methodologies]: Artificial Intelligence Problem Solving, Control Methods, and Search General Terms: Algorithms

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Ján Antolík, William H. Hsu
• Keywords: GECCO 2005, Genetic Programming, Parity Problem, Tree-like Phylogenetic Pattern