— David Wolpert and William G. Macready "No free lunch theorems for optimization", IEEE Trans. Evol. Comp. 1(1) (1997): 67-82.
The inability of any evolutionary search procedure to perform better than average indicate[s] the importance of incorporating problem-specific knowledge into the behavior of the [search] algorithm.
According to conservation of information theorems, performance of an arbitrarily chosen search, on average, does no better than blind search. Domain expertise and prior knowledge about search space structure or target location is therefore essential in crafting the search algorithm. The effectiveness of a given algorithm can be measured by the active information introduced to the search. We illustrate this by identifying sources of active information in Avida, a software program designed to search for logic functions using nand gates. Avida uses stair step active information by rewarding logic functions using a smaller number of nands to construct functions requiring more. Removing stair steps deteriorates Avida's performance while removing deleterious instructions improves it. Some search algorithms use prior knowledge better than others. For the Avida digital organism, a simple evolutionary strategy generates the Avida target in far fewer instructions using only the prior knowledge available to Avida.[ PDF | IEEE ]