Evolving caching algorithms in C by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Norman Paterson and Mike Livesey",
  title =        "Evolving caching algorithms in C by genetic
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and 
                 David B. Fogel and Max Garzon and Hitoshi Iba and 
                 Rick L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "genetic algorithms, genetic programming, GADS, BNF
                 grammar, GAGS-1.0, linear GP",
  pages =        "262--267",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www.dcs.st-and.ac.uk/~norman/Pubs/cache.ps.gz",
  size =         "6 pages",
  abstract =     "This paper outlines current work in ontogenic-mapped
                 and language abstracted genetic programming.
                 Developments in the genetic algorithm for deriving
                 software (GADS) technique are described, and tested in
                 a series of experiments to generate caching algorithms
                 in C. GADS quickly finds over-fitted solutions which
                 perform better than designed solutions but only in one
                 niche. The need for a scalable approach in GADS to deal
                 with language definitions involving more productions is
  notes =        "GP-97. Test data also used in

                 Production weights on BNF grammar. No mutation. GP
                 returns long victim which forced into legal cache line
                 range by wrapper which gives cache line of data to be
                 ejected from cache. Evolved C code decides who to evict
                 from memory access cache. INFO array, read() write_x,
                 small_x, large_x, random_x, counter, CACHESIZE, div,
                 rem. phenotype.cc Linear bitstring (2500 or 3000 bits)
                 GA (5 or 6 bits per BNF production. Ontogenic mapping.
                 Repair. BNF text converted to C++ data structure.
                 Number of productions varies with each BNF rule (up to
                 2**6). Uses multiple identical replicated productions
                 of some BNF rules. Each C++ phenotype compiled for
                 fitness calculation using trace file (Flanagan,

Genetic Programming entries for Norman R Paterson Mike Livesey