@TechReport{Langdon97,
  author      = "William B Langdon and Riccardo Poli",
  title       = {Genetic Programming Bloat with Dynamic Fitness},
  institution = {University of Birmingham, School of Computer Science},
  number      = {CSRP-97-29},
  month       = {December},
  year        = {1997},
  email       = {W.B.Langdon@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk},
  file        = {/1997/CSRP-97-29.ps.gz},
  url         = {ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-29.ps.gz},
  abstract    = {In artificial evolution individuals which perform as their
                 parents are usually rewarded identically to their parents.
                 We note that Nature is more dynamic and there may be a
                 penalty to pay for doing the same thing as your parents.
                 We report two sets of experiments where static fitness
                 functions are firstly augmented by a penalty for unchanged
                 offspring and secondly the static fitness case is replaced
                 by randomly generated dynamic test cases. We conclude
                 genetic programming, when evolving artificial ant control
                 programs, is surprisingly little effected by large
                 penalties and program growth is observed in all our
                 experiments.
                 },
}