%0 Conference Paper %1 poli:2005:eurogp %A Poli, Riccardo %A Langdon, William B. %A Holland, Owen %B Proceedings of the 8th European Conference on Genetic Programming %C Lausanne, Switzerland %D 2005 %E Keijzer, Maarten %E Tettamanzi, Andrea %E Collet, Pierre %E van Hemert, Jano I. %E Tomassini, Marco %I Springer %K algorithms, genetic programming %P 291--300 %T Extending Particle Swarm Optimisation via Genetic Programming %U http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=291 %V 3447 %X Genetic programming is used to evolve Particle Swarm Optimisers (PSOs). PSOs include a small number of interacting particles, which fly over the fitness landscape in search for high fitness points. The particles are typically controlled by forces which encourage each particle to fly back towards the best point on the landscape sampled by it (personal best) while at the same time trying to imitate the best particle in the swarm with a drive towards the swarm's best. The standard PSO is well known for its effectiveness on a variety of optimisation problems. We explore the possibility of evolving the force generating equations to control the particles in a PSO. Our aim is to verify the feasibility of this approach and to start exploring what types of PSOs are most appropriate for different classes of landscapes. %@ 3-540-25436-6

@inproceedings{poli:2005:eurogp, abstract = {Genetic programming is used to evolve Particle Swarm Optimisers (PSOs). PSOs include a small number of interacting particles, which fly over the fitness landscape in search for high fitness points. The particles are typically controlled by forces which encourage each particle to fly back towards the best point on the landscape sampled by it (personal best) while at the same time trying to imitate the best particle in the swarm with a drive towards the swarm's best. The standard PSO is well known for its effectiveness on a variety of optimisation problems. We explore the possibility of evolving the force generating equations to control the particles in a PSO. Our aim is to verify the feasibility of this approach and to start exploring what types of PSOs are most appropriate for different classes of landscapes.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Lausanne, Switzerland}, author = {Poli, Riccardo and Langdon, William B. and Holland, Owen}, bibsource = {DBLP, http://dblp.uni-trier.de}, biburl = {https://www.bibsonomy.org/bibtex/2070df8a1a64ef777f860e822515c6893/brazovayeye}, booktitle = {Proceedings of the 8th European Conference on Genetic Programming}, editor = {Keijzer, Maarten and Tettamanzi, Andrea and Collet, Pierre and {van Hemert}, Jano I. and Tomassini, Marco}, interhash = {cda3ef22d05f07da84d5d22412984d15}, intrahash = {070df8a1a64ef777f860e822515c6893}, isbn = {3-540-25436-6}, keywords = {algorithms, genetic programming}, month = {30 March - 1 April}, notes = {Also known as eurogp:PoliLH05 Part of \cite{keijzer:2005:GP} EuroGP'2005 held in conjunction with EvoCOP2005 and EvoWorkshops2005}, organisation = {EvoNet}, pages = {291--300}, publisher = {Springer}, publisher_address = {Berlin}, series = {Lecture Notes in Computer Science}, size = {10 pages}, timestamp = {2008-06-19T17:49:47.000+0200}, title = {Extending Particle Swarm Optimisation via Genetic Programming}, url = {http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=291}, volume = 3447, year = 2005 }