Kolmogorov complexity, Optimization and Hardness

The Kolmogorov complexity (KC) of a string is defined as the length of the shortest program that can print that string and halts. This measure of complexity is often used in optimization to indicate expected function difficulty. While it is often used, there are known counterexamples. This paper investigates the applicability of KC as an estimator of problem difficulty for optimization in the black box scenario. In particular we address the known counterexamples (e.g., pseudorandom functions, the NIAH) and explore the connection of KC to the NFLTs. We conclude that high KC implies hardness however, while easy fitness functions have low KC the reverse is not necessarily true.