- author = "Anna I. Esparcia-Alcazar and Ken Sharman",
- title = "Learning Schemes for Genetic Programming",
- booktitle = "Late Breaking Papers at the 1997 Genetic Programming Conference",
- year = "1997",
- editor = "John R. Koza",
- pages = "57--65",
- address = "Stanford University, CA, USA",
- publisher_address = "Stanford University, Stanford, California, 94305-3079, USA",
- month = "13--16 " # jul,
- publisher = "Stanford Bookstore",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "0-18-206995-8",
-
URL = "
http://www.iti.upv.es/~anna/papers/learningGP97.ps",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/esparcia-alcazar/Esparcia-Alcazar_1997_lsGP.pdf",
- size = "9 pages",
-
abstract = "A learning capability is introduced in the Genetic
Programming (GP) paradigm. This is achieved by
enhancing GP with Simulated Annealing (SA), where the
latter adapts the parameter values (in the form of node
gains) in the structures evolved by the former. A
special feature of this approach is that, due to the
particularities of the representation used, it allows
engineering problems (in which numerical parameters are
important) to be addressed, thus extending the
applicability of the GP paradigm.
We study two different learning schemes, which we refer to as Darwinian and Lamarckian according to whether the learned node gains are inherited or not. We compare the results obtained by these two techniques to those obtained in the absence of learning (both with node gain representation and standard GP representation). The results show the great interest of both learning schemes.
The application presented is a classical Digital Signal Processing problem: the equalisation of a noisy communications channel.",
- notes = "GP-97LB The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670",
