Faster Genetic Programming based on Local Gradient Search of Numeric Leaf Values

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{topchy:2001:gecco,
  title =        "Faster Genetic Programming based on Local Gradient
                 Search of Numeric Leaf Values",
  author =       "Alexander Topchy and William F. Punch",
  pages =        "155--162",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and 
                 W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and 
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and 
                 Max H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, gradient
                 optimization, algorithmic, differentiation, Baldwin
                 effect, Lamarckian learning, symbolic regression",
  ISBN =         "1-55860-774-9",
  URL =          "http://garage.cse.msu.edu/papers/GARAGe01-07-01.pdf",
  URL =          "http://citeseer.ist.psu.edu/523881.html",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf",
  abstract =     "We examine the effectiveness of gradient search
                 optimization of numeric leaf values for Genetic
                 Programming. Genetic search for tree-like programs at
                 the population level is complemented by the
                 optimization of terminal values at the individual
                 level. Local adaptation of individuals is made easier
                 by algorithmic differentiation. We show how
                 conventional random constants are tuned by gradient
                 descent with minimal overhead. Several experiments with
                 symbolic regression problems are performed to
                 demonstrate the approach's effectiveness. Effects of
                 local learning are clearly manifest in both improved
                 approximation accuracy and selection changes when
                 periods of local and global search are interleaved.
                 Special attention is paid to the low overhead of the
                 local gradient descent. Finally, the inductive bias of
                 local learning is quantified.",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of \cite{spector:2001:GECCO}",
}

Genetic Programming entries for Alexander Topchy William F Punch

Citations