- author = "Riccardo Poli and William B. Langdon",
- title = "Sub-machine-code Genetic Programming",
- booktitle = "Advances in Genetic Programming 3",
- publisher = "MIT Press",
- year = "1999",
- editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline",
- chapter = "13",
- pages = "301--323",
- address = "Cambridge, MA, USA",
- month = jun,
- keywords = "genetic algorithms, genetic programming",
- ISBN = "0-262-19423-6",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch13.pdf",
-
URL = "http://cswww.essex.ac.uk/staff/rpoli/papers/Poli-AIGP3-1999.pdf",
-
URL = "
http://citeseer.ist.psu.edu/332925.html",
-
DOI = "
doi:10.7551/mitpress/1110.003.0018",
- abstract = "Introduction Genetic Programming (GP) [Koza, 1992; Koza, 1994; Banzhaf et al., 1998] is usually seen as quite demanding from the computation load and memory use point of view. So, over the years a number of ideas on how to improve GP performance have been proposed in the literature. We recall the main speedup techniques published to date in Section 13.2. Some of these techniques are now used in many GP implementations. Thanks to this and to the fact that the power of our workstations is increasing exponentially (today's CPUs are now more than 10 times faster than those used in early GP work), nowadays we can run 50 generations a typical GP benchmark problem with a population of 500 individuals in perhaps ten seconds on a normal workstation. Nonetheless, the demand for more and more efficient implementations has not stopped. This is because extensive experimental GP studies (like [Langdon and Poli, 1998] or [Luke and Spector, 1998]) and complex applications (like [Poli, 1996]",
-
notes = "AiGP3 See http://cognet.mit.edu
Machine code level parallelism. Includes code.",
