- author = "William B. Langdon and Riccardo Poli",
- title = "Finding Social Landscapes for PSOs via Kernels",
- booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation",
- year = "2006",
- editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas",
- pages = "6118--6125",
- address = "Vancouver",
- month = "16-21 " # jul,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, PSO, XPS",
- ISBN = "0-7803-9487-9",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2006.pdf",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2006.ps.gz",
-
URL = "http://ieeexplore.ieee.org/iel5/11108/35623/01688507.pdf?tp=&arnumber=1688507&isnumber=35623",
-
DOI = "
doi:10.1109/CEC.2006.1688507",
- size = "8 pages",
-
abstract = "Particle swarm optimiser and genetic algorithm
populations are macro-organisms, which perceive their
environment as if filtered via a kernel. The kernel
assimilates each individual's sensory abilities so that
the collective moves using a greedy hill-climbing
strategy. This model is fitted to data collected in
real PSO and GA runs by using genetic programming to
evolve the kernel.
In nature animals tend to live within groups. The social interactions effectively transform the fitness selection landscape seen by an isolated individual. In some cases a group behaves (or even can be said to think) like a single organism. Kernels provide a lens which coarse-grains or averages individual senses and so may help explain joint actions and social responses.
The original multi-modal problem is smoothed by convolving it with a problem specific filter designed by GP. Because populations see the transformed social fitness landscape, they can pass over local optima. GP can give a good fit between the predicted behaviour of the macroscopic organism and the actual runs.",
-
notes = "See \cite{langdon:2006:TEC}
WCCI 2006 - A joint meeting of the IEEE, the EPS, and the IEE.
IEEE Catalog Number: 06TH8846D",
