Tree swarm optimization: an approach to PSO-based tree discovery

In recent years a swarm-based optimization methodology called particle swarm optimization (PSO) has developed. PSO is highly explorative and primarily used in function optimization. This paper proposes a swarm-based learning algorithm based on PSO which is able to discover trees in tree spaces. Particles are flying through a tree space forming flocks around peaks of a fitness function. Because it inherits the explorative property of PSO, it needs only few evaluations to find suitable trees.

[1]  Mario Köppen,et al.  Data Swarm Clustering , 2006, Swarm Intelligence in Data Mining.

[2]  D. Essam,et al.  AntTAG : a further study , 2002 .

[3]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[4]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  Walter A. Kosters,et al.  Detecting and Pruning Introns for Faster Decision Tree Evolution , 2004, PPSN.

[6]  Hussein A. Abbass,et al.  AntTAG: a new method to compose computer programs using colonies of ants , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).