- author = "Riccardo Poli and Cecilia {Di Chio} and William B. Langdon",
- title = "Exploring extended particle swarms: a genetic programming approach",
- booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation",
- year = "2005",
- editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler",
- volume = "1",
- ISBN = "1-59593-010-8",
- pages = "169--176",
- address = "Washington DC, USA",
-
URL = "
http://www.cs.essex.ac.uk/staff/poli/papers/geccopso2005.pdf",
-
URL = "http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p169.pdf",
-
DOI = "
10.1145/1068009.1068036",
- publisher = "ACM Press",
- publisher_address = "New York, NY, 10286-1405, USA",
- month = "25-29 " # jun,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- keywords = "genetic algorithms, genetic programming, Swarm Intelligence, particle swarm optimisation, PSO, performance",
- size = "8 pages",
-
abstract = "Particle Swarm Optimisation (PSO) uses a population of
particles fly over the fitness landscape in search of
an optimal solution. The particles are controlled by
forces that encourage each particle to fly back both
towards the best point sampled by it and towards the
swarm's best point, while its momentum tries to keep it
moving in its current direction.
Previous research \cite{poli:2005:eurogp} started exploring the possibility of evolving the force generating equations which control the particles through the use of genetic programming (GP).
We independently verify the findings of \cite{poli:2005:eurogp} and then extend it by considering additional meaningful ingredients for the PSO force-generating equations, such as global measures of dispersion and position of the swarm. We show that, on a range of problems, GP can automatically generate new PSO algorithms that outperform standard human-generated as well as some previously evolved ones.",
-
notes = "GECCO-2005 A joint meeting of the fourteenth
international conference on genetic algorithms
(ICGA-2005) and the tenth annual genetic programming
conference (GP-2005).
ACM Order Number 910052, XPS, ACM gecco-2005 key 1068036",
