%0 Conference Paper %1 Haynes:1998:CRS %A Haynes, Thomas %B Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming %C Mission Valley Marriott, San Diego, California, USA %D 1998 %E Porto, V. William %E Saravanan, N. %E Waagen, D. %E Eiben, A. E. %I Springer-Verlag %K algorithms, genetic programming %P 683--692 %R doi:10.1007/BFb0040819 %T A Comparision of Random Search versus Genetic Programming as Engines for Collective Adaptation %V 1447 %X We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the two components is actually contributing to the search process. We investigate a collective memory search which uses a random search engine and find that it significantly outperforms the GP based search engine. We examine the solution space and show that as problem complexity and search space grow, a collective adaptive system will perform better than a collective memory search employing random search as an engine. %@ 3-540-64891-7

@inproceedings{Haynes:1998:CRS, abstract = {We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the two components is actually contributing to the search process. We investigate a collective memory search which uses a random search engine and find that it significantly outperforms the GP based search engine. We examine the solution space and show that as problem complexity and search space grow, a collective adaptive system will perform better than a collective memory search employing random search as an engine.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Mission Valley Marriott, San Diego, California, USA}, author = {Haynes, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/2543065f0865c9f03079682c9102edba0/brazovayeye}, booktitle = {Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming}, broken = {http://www.cs.twsu.edu/~haynes/random.ps}, doi = {doi:10.1007/BFb0040819}, editor = {Porto, V. William and Saravanan, N. and Waagen, D. and Eiben, A. E.}, interhash = {fb1ea51b04f9890ac23865a67f5ce24a}, intrahash = {543065f0865c9f03079682c9102edba0}, isbn = {3-540-64891-7}, keywords = {algorithms, genetic programming}, month = {25-27 March}, notes = {EP-98. {"}With collective adaptation{"}.... {"}A random search engine is more effective than a GP based one, but only at low problem complexity. As the complexity increases, the competetiveness of the GP search engine is more effective than the wide ranging exploration of random search.{"} pages 10-11.}, organisation = {Natural Selection, Inc.}, pages = {683--692}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {LNCS}, size = {10 pages}, timestamp = {2008-06-19T17:41:13.000+0200}, title = {A Comparision of Random Search versus Genetic Programming as Engines for Collective Adaptation}, volume = 1447, year = 1998 }