%0 Conference Paper %1 1068096 %A Williams, Nathan %A Mitchell, Melanie %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 Coevolution, algorithms, evolution genetic programming, resource sharing, spatial %P 523--530 %T Investigating the success of spatial coevolution %U http://doi.acm.org/10.1145/1068009.1068096 %V 1 %X We investigate the results of coevolution of spatially distributed populations. In particular, we describe work in which a simple function approximation problem is used to compare different spatial evolutionary methods. Our work shows that, on this problem, spatial coevolution is dramatically more successful than any other spatial evolutionary scheme we tested. Our results support two hypotheses about the source of spatial coevolution's superior performance: (1) spatial coevolution allows population diversity to persist over many generations; and (2) spatial coevolution produces training examples ("parasites") that specifically target weaknesses in models ("hosts"). The precise mechanisms by which the combination of spatial embedding and coevolution produces these results are still not well understood. %@ 1-59593-010-8

@inproceedings{1068096, abstract = {We investigate the results of coevolution of spatially distributed populations. In particular, we describe work in which a simple function approximation problem is used to compare different spatial evolutionary methods. Our work shows that, on this problem, spatial coevolution is dramatically more successful than any other spatial evolutionary scheme we tested. Our results support two hypotheses about the source of spatial coevolution's superior performance: (1) spatial coevolution allows population diversity to persist over many generations; and (2) spatial coevolution produces training examples ({"}parasites{"}) that specifically target weaknesses in models ({"}hosts{"}). The precise mechanisms by which the combination of spatial embedding and coevolution produces these results are still not well understood.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Washington DC, USA}, author = {Williams, Nathan and Mitchell, Melanie}, biburl = {https://www.bibsonomy.org/bibtex/21b6b956c520539f09e99fde373b75900/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 = {4d0180c92cda6923d139159dedf90e18}, intrahash = {1b6b956c520539f09e99fde373b75900}, isbn = {1-59593-010-8}, keywords = {Coevolution, algorithms, evolution genetic programming, resource sharing, spatial}, 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}, organisation = {ACM SIGEVO (formerly ISGEC)}, pages = {523--530}, publisher = {ACM Press}, publisher_address = {New York, NY, 10286-1405, USA}, size = {8 pages}, timestamp = {2008-06-19T17:54:12.000+0200}, title = {Investigating the success of spatial coevolution}, url = {http://doi.acm.org/10.1145/1068009.1068096}, volume = 1, year = 2005 }