🚀 go-pugleaf

   A function optimizer has a global standard of fitness, the height of the
function being optimized.  A GA playing prisoner's dillemma has a changing
fitness function based on the particular mix of player's in the algorithm.
I call these two sorts of genetic algorithms evolutionary and coevolutionary.

   What I'd like is to see references to transformations of evolutionary 
problems into coevolutionary problems.  If possible summarize the effects.
For example:

   Hillis, Daniel "Co-evolving Parasites Inprove Simulated Evolution as an 
   Optimization Procedure" Proceedings of the Second Conference on Artificial
   Life.

Summary:
 
   Hillis observed better results when he modified a GA that located sorting
networks with relatively few comparitors to have an evolving population of test
sequences whoes fitness was proportional to the number of sorting networks they
fooled.  The fixed population of test sequences let to a 65 comparitor network
for a 16-element list.  The coevolutionary version found a 61 element network.
It should be noted that the "best" network contains 60 comparitors and was
found by a human being.

   Thanks in advance,

Dan
Danwell@IASTAET.EDU