- author = "Christian Veenhuis and Mario K{\"o}ppen and J{\"o}rg Kr{\"u}ger and Bertram Nickolay",
- title = "Tree Swarm Optimization: An Approach to PSO-based Tree Discovery",
- booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation",
- year = "2005",
- editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer",
- volume = "2",
- pages = "1238--1245",
- address = "Edinburgh, UK",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "2-5 " # sep,
- organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, PSO, TSO",
- ISBN = "0-7803-9363-5",
-
DOI = "
doi:10.1109/CEC.2005.1554832",
- abstract = "In recent years a swarm-based optimisation methodology called Particle Swarm Optimisation (PSO) has developed. PSO is highly explorative and primarily used in function optimisation. 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.",
-
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.
symbolic regression, artificial ant, TSO. 'Tree swarm optimisation uses full trees for the particles.' All members of the swarm have the same structure and length. Number of dimensions in continious PSO space = size of terminal set + size of function set. No attempt at placing similar symbols near each other. 'position trees' and 'velocity trees'. Section 3.5 'Distances between full symbol trees' Four symbolic regression. 'But as in PSO the exact global optimum cannot be found very often for difficult problems.' Santa Fe artificial ant. 'In 64 percent of the runs, solutions collecting all food items are found.' UCI iris",
