  According to the No Free Lunch (NFL) theorems all black-box algorithms
perform equally well when compared over the entire set of optimization
problems. An important problem related to NFL is finding a test problem for
which a given algorithm is better than another given algorithm. Of high
interest is finding a function for which Random Search is better than another
standard evolutionary algorithm. In this paper, we propose an evolutionary
approach for solving this problem: we will evolve test functions for which a
given algorithm A is better than another given algorithm B. Two ways for
representing the evolved functions are employed: as GP trees and as binary
strings. Several numerical experiments involving NFL-style Evolutionary
Algorithms for function optimization are performed. The results show the
effectiveness of the proposed approach. Several test functions for which Random
Search performs better than all other considered algorithms have been evolved.
