@TechReport{Langdon98,
  author      = "William B Langdon and Riccardo Poli",
  title       = {Better Trained Ants for Genetic Programming},
  institution = {University of Birmingham, School of Computer Science},
  number      = {CSRP-98-12},
  month       = {April},
  year        = {1998},
  email       = {W.B.Langdon@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk},
  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)
                 },
}