- author = "Thomas Loveard",
- title = "Genetic Programming With Meta-Search: Searching For a Successful Population Within The Classification Domain",
- booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
- year = "2003",
- editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa",
- volume = "2610",
- series = "LNCS",
- pages = "119--129",
- address = "Essex",
- publisher_address = "Berlin",
- month = "14-16 " # apr,
- organisation = "EvoNet",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "3-540-00971-X",
- language = "en",
- oai = "oai:CiteSeerX.psu:10.1.1.534.3317",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.534.3317",
-
URL = "
http://goanna.cs.rmit.edu.au/~vc/papers/eurogp-03.pdf",
-
DOI = "
10.1007/3-540-36599-0_11",
-
abstract = "The genetic programming (GP) search method can often
vary greatly in the quality of solution derived from
one run to the next. As a result, it is often the case
that a number of runs must be performed to ensure that
an effective solution is found. This paper introduces
several methods which attempt to better use the
computational resources spent on performing a number of
independent GP runs. Termed meta-search strategies,
these methods seek to search the space of evolving GP
populations in an attempt to focus computational
resources on those populations which are most likely to
yield competitive solutions. Two meta-search strategies
are introduced and evaluated over a set of
classification problems. The meta-search strategies are
termed a pyramid search strategy and a population beam
search strategy. Additional to these methods, a
combined approach using properties of both the pyramid
and population beam search methods is evaluated.
Over a set of five classification problems, results show that meta-search strategies can substantially improve the accuracy of solutions over those derived by a set of independent GP runs. In particular the combined approach is demonstrated to give more accurate classification performance whilst requiring less time to train than a set of independent GP runs, making this method a promising approach for problems for which multiple GP runs must be performed to ensure a quality solution.",
- notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003",
