- author = "William B. Langdon and Riccardo Poli",
- title = "Evolving Problems to Learn about Particle Swarm and other Optimisers",
- 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 = "1",
- pages = "81--88",
- address = "Edinburgh, UK",
- month = "2-5 " # sep,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, PSO",
- ISBN = "0-7803-9363-5",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2005.pdf",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2005.ps.gz",
-
DOI = "
doi:10.1109/CEC.2005.1554670",
- size = "8 pages",
- abstract = "We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular we analyse Particle Swarm Optimization (PSO) and Differential Evolution (DE). Both evolutionary algorithms are contrasted with a robust deterministic gradient based searcher (based on Newton-Raphson). The fitness landscapes made by genetic programming (GP) are used to illustrate difficulties in GAs and PSOs thereby explaining how they work and allowing us to devise better extended particle swarm systems (XPS).",
-
notes = "CEC2005 - A joint meeting of the IEEE, the EPS, and
the IEE.
Shorter version available as \cite{langdon:2005:CECb2p}.",
