%0 Conference Paper %1 dichio:2005:gecco %A Poli, Riccardo %A Di Chio, Cecilia %A Langdon, William B. %B GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation %C Washington DC, USA %D 2005 %E Beyer, Hans-Georg %E O'Reilly, Una-May %E Arnold, Dirk V. %E Banzhaf, Wolfgang %E Blum, Christian %E Bonabeau, Eric W. %E Cantu-Paz, Erick %E Dasgupta, Dipankar %E Deb, Kalyanmoy %E Foster, James A. %E de Jong, Edwin D. %E Lipson, Hod %E Llora, Xavier %E Mancoridis, Spiros %E Pelikan, Martin %E Raidl, Guenther R. %E Soule, Terence %E Tyrrell, Andy M. %E Watson, Jean-Paul %E Zitzler, Eckart %I ACM Press %K Intelligence, PSO, Swarm algorithms, genetic optimisation, particle performance programming, swarm %P 169--176 %T Exploring extended particle swarms: a genetic programming approach %U http://doi.acm.org/10.1145/1068009.1068036 %V 1 %X 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 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 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. %@ 1-59593-010-8

@inproceedings{dichio:2005:gecco, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Washington DC, USA}, author = {Poli, Riccardo and {Di Chio}, Cecilia and Langdon, William B.}, biburl = {https://www.bibsonomy.org/bibtex/2924afc7cab84a865c2dcb5237f7f5e84/brazovayeye}, booktitle = {{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation}, editor = {Beyer, Hans-Georg and O'Reilly, Una-May and Arnold, Dirk V. and Banzhaf, Wolfgang and Blum, Christian and Bonabeau, Eric W. and Cantu-Paz, Erick and Dasgupta, Dipankar and Deb, Kalyanmoy and Foster, James A. and {de Jong}, Edwin D. and Lipson, Hod and Llora, Xavier and Mancoridis, Spiros and Pelikan, Martin and Raidl, Guenther R. and Soule, Terence and Tyrrell, Andy M. and Watson, Jean-Paul and Zitzler, Eckart}, interhash = {20c3a48e9729ac517116bc911f626366}, intrahash = {924afc7cab84a865c2dcb5237f7f5e84}, isbn = {1-59593-010-8}, keywords = {Intelligence, PSO, Swarm algorithms, genetic optimisation, particle performance programming, swarm}, month = {25-29 June}, 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}, organisation = {ACM SIGEVO (formerly ISGEC)}, pages = {169--176}, publisher = {ACM Press}, publisher_address = {New York, NY, 10286-1405, USA}, size = {8 pages}, timestamp = {2008-06-19T17:38:40.000+0200}, title = {Exploring extended particle swarms: a genetic programming approach}, url = {http://doi.acm.org/10.1145/1068009.1068036}, volume = 1, year = 2005 }