- author = "Nathan Williams and Melanie Mitchell",
- title = "Investigating the success of spatial coevolution",
- 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 = "523--530",
- address = "Washington DC, USA",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2005/docs/p523.pdf",
-
DOI = "
doi:10.1145/1068009.1068096",
- 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, Coevolution, resource sharing, spatial evolution",
- size = "8 pages",
- 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.",
-
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",
