Better Trained Ants for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@TechReport{langdon:1998:antlook2TR,
  author =       "W. B. Langdon and R. Poli",
  title =        "Better Trained Ants for Genetic Programming",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-98-12",
  month =        apr,
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming,
                 multi-objective GP",
  file =         "/1998/CSRP-98-12.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-12.ps.gz",
  abstract =     "The problem of programming an artificial ant to follow
                 the Santa Fe trail has been repeatedly used as a
                 benchmark problem in GP. Recently we have shown
                 performance of several techniques is not much better
                 than the best performance obtainable using uniform
                 random search. We suggested that this could be because
                 the program fitness landscape is difficult for hill
                 climbers and the problem is also difficult for Genetic
                 Algorithms as it contains multiple levels of
                 deception.

                 Here we redefine the problem so the ant is (1) obliged
                 to traverse the trail in approximately the correct
                 order, (2) to find food quickly. We also investigate
                 (3) including the ant's speed in the fitness function,
                 either as a linear addition or as a second objective in
                 a multi-objective fitness function, and (4) GP one
                 point crossover.

                 A simple genetic programming system, with no size or
                 depth restriction, is shown to perform approximately
                 three times better with the improved training function.
                 (Extends CSRP-98-08 \cite{langdon:1998:antlook})",
}

Genetic Programming entries for William B Langdon Riccardo Poli