@TechReport{Langdon98,
  author      = "William B Langdon",
  title       = {Better Trained Ants},
  institution = {University of Birmingham, School of Computer Science},
  number      = {CSRP-98-08},
  month       = {February},
  year        = {1998},
  email       = {W.B.Langdon@cs.bham.ac.uk},
  file        = {/1998/CSRP-98-08.ps.gz},
  url         = {ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-08.ps.gz},
  abstract    = {The problem of programming an artificial ant to follow the
                 Santa~Fe trail has been repeatedly used as a benchmark
                 problem. 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 obliged to
                 traverse the trail in approximately the correct order. A
                 simple genetic programming system, with no size or depth
                 restriction, is show to perform approximately three times
                 better with the improved training function.
                 },
}