The No Free Lunch Theorem, often abbreviated as NFL or NFLT, is a theoretical finding that suggests all optimization algorithms perform equally well when their performance is averaged over all possible objective functions.

The NFL stated that within certain constraints, over the space of all possible problems, every optimization technique will perform as well as every other one on average (including Random Search)

— Page 203, Essentials of Metaheuristics, 2011.

The theorem applies to optimization generally and to search problems, as optimization can be described or framed as a search problem.

The implication is that the performance of your favorite algorithm is identical to a completely naive algorithm, such as random search.

Roughly speaking we show that for both static and time dependent optimization problems the average performance of any pair of algorithms across all possible problems is exactly identical.

— No Free Lunch Theorems For Optimization, 1997.

An easy way to think about this finding is to consider a large table like you might have in excel.

Across the top of the table, each column represents a different optimization algorithm. Down the side of the table, each row represents a different objective function. Each cell of the table is the performance of the algorithm on the objective function, using whatever performance measure you like, as long as it is consistent across the whole table.

Depiction on the No Free Lunch Theorem as a Table of Algorithms and Problems

You can imagine that this table will be infinitely large. Nevertheless, in this table, we can calculate the average performance of any algorithm from all the values in its column and it will be identical to the average performance of any other algorithm column.