The problem of evolving an artificial ant to follow the Santa Fe trail is used to demonstrate the well known genetic programming feature of growth in solution length. Known variously as “bloat”, “redundancy”, “introns”, “fluff”, “Structural Complexity” with antonyms “parsimony”, “Minimum Description Length” (MDL) and “Occam’s razor”. Comparison with runs with and without fitness selection pressure shows the tendency for solutions to grow in size is caused by fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. Since with simple static evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long representations of a given solution than short ones of the same solution. Thus with an unbiased random search we expect longer representations to occur more often and so representation length tends to increase. I.e. fitness based selection leads to bloat.