Bounding the number of favorable functions in stochastic search

According to the No Free Lunch theorems for search, when uniformly averaged over all possible search functions, every search algorithm has identical search performance for a wide variety of common performance metrics [1], [2], [3], [4]. Differences in performance can arise, however, between two algorithms when performance is measured over non-closed under permutation sets of functions, such as sets consisting of a single function. Using uniform random sampling with replacement as a baseline, we ask how many functions exist such that a search algorithm has better expected performance than random sampling. We define favorable functions as those that allow an algorithm to locate a search target with higher probability than uniform random sampling with replacement, and we bound the proportion of favorable functions for stochastic search methods, including genetic algorithms. Using active information [5] as our divergence measure, we demonstrate that no more than 2
^-b
of all functions are favorable by b or more bits, for b ≥ 2 and reasonably sized search spaces (n ≥ 19). Thus, the proportion of functions for which an algorithm performs relatively well by a moderate degree is strictly bounded. Our results can be viewed as statement of information conservation [6], [7], [1], [8], [5], since identifying a favorable function of b or more bits requires at least b bits of information, under the conditions given.