- author = "Eva Alfaro-Cid and Ken Sharman and Anna I. Esparcia-Alcazar",
- title = "Evolving a Learning Machine by Genetic Programming",
- booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation",
- year = "2006",
- editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas",
- pages = "958--962",
- address = "Vancouver",
- month = "16-21 " # jul,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, simulated annealing, function set, learning machine, learning node, optimization algorithm, simulated annealing",
- ISBN = "0-7803-9487-9",
-
DOI = "
doi:10.1109/CEC.2006.1688316",
- size = "5 pages",
- abstract = "We describe a novel technique for evolving a machine that can learn. The machine is evolved using a Genetic Programming (GP) algorithm that incorporates in its function set what we have called a learning node. Such a node is tuned by a second optimisation algorithm (in this case Simulated Annealing), mimicking a natural learning process and providing the GP tree with added flexibility and adaptability. The result of the evolution is a system with a fixed structure but with some variable parameters. The system can then learn new tasks in new environments without undergoing further evolution.",
-
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages as 254--258",
