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Challenges are often lofted to explain how gradualistic evolution can evolve systems where function ceases with the removal of any of their multiple parts. We present a genetic algorithm (GA) example using a dynamic fitness function. Given clear definitions of relevant terms, the GA produces such complex systems.

In this paper, we provide an algorithm that systematically considers all small trees in the search space of genetic programming. These small trees are used to generate useful subroutines for genetic programming. This algorithm is tested on the Artificial Ant on the Santa Fe Trail problem, a venerable problem for genetic programming systems. When four levels of iteration are used, the algorithm presented here generates better results than any known published result by a factor of 7.