- author = "Marcin Suchorzewski",
- title = "Extending Genetic Programming to Evolve Perceptron-Like Learning Programs",
- booktitle = "10th International Conference Artificial Intelligence and Soft Computing, ICAISC 2010, Part II",
- year = "2010",
- editor = "Leszek Rutkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada",
- volume = "6114",
- series = "Lecture Notes in Computer Science",
- pages = "221--228",
- address = "Zakopane, Poland",
- month = jun # " 13-17",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, evolutionary neural networks, learning programs, supervised learning",
- isbn13 = "978-3-642-13231-5",
- bibdate = "2010-06-22",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2010-2.html#Suchorzewski10",
-
URL = "
http://dx.doi.org/10.1007/978-3-642-13232-2",
-
DOI = "
doi:10.1007/978-3-642-13232-2_27",
- size = "8 pages",
- abstract = "We extend genetic programming (GP) with a local memory and vectorisation to evolve simple, perceptron-like programs capable of learning by error correction. The local memory allows for a scalar value or vector to be stored and manipulated within a local scope of GP tree. Vectorisation consists in grouping input variables and processing them as vectors. We demonstrate these extensions, along with an island model, allow to evolve general perceptron-like programs, i.e. working for any number of inputs. This is unlike in standard GP, where inputs are represented explicitly as scalars, so that scaling up the problem would require to evolve a new solution. Moreover, we find vectorisation allows to represent programs more compactly and facilitates the evolutionary search.",
-
notes = "ICAISC 2010
Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland",
