- author = "Thomas Haynes",
- title = "A Comparision of Random Search versus Genetic Programming as Engines for Collective Adaptation",
- editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben",
- booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming",
- year = "1998",
- volume = "1447",
- series = "LNCS",
- pages = "683--692",
- address = "Mission Valley Marriott, San Diego, California, USA",
- publisher_address = "Berlin",
- month = "25-27 " # mar,
- organisation = "Natural Selection, Inc.",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "3-540-64891-7",
- broken = "http://www.cs.twsu.edu/~haynes/random.ps",
-
DOI = "
doi:10.1007/BFb0040819",
- size = "10 pages",
- 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.",
-
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.",
