Or universal search, or Lsearch

See Algorithmic information theory

Suppose there exists an algorithm, A, that can examine M and x, then print out p within time T. Levin had a search procedure (Levin search) that, without knowing A could do the same thing that A did, but in no more time than CT2^L Here, L is the length of the shortest description of A, using a suitable reference machine, and C is a measure of how much slower the reference machine is than a machine that implements A directly. An alternative form of this cost of the search is CT/P. Here P = 2^L is approximately the a priori probability of the algorithm A.

$T/P$ is called "conceptual jump soze" by Solomonoff.

Though Lsearch has been widely described (Lev 73; Sol 84,86,89; Li 93 pp. 410-413) there has been little application of it to real problem solving. Paul and Solomonoff (Pau 94) discuss its application to several problems and calculate T/P (Conceptual Jump Size) for solutions but Schmidhuber (Schm 94) was perhaps the first to actually run a computer program that used Lsearch to solve problems. While it only solved simple problems in neural net design the technique used is very general and of much interest. The probabilistic version of Lsearch used in the program had a serious error in it, but it has been replaced with a more conventional non-probabilistic Lsearch that seems to work fine

See this vid too.

See Universal AI for more general techniques, inspired on this.

The problem with Levin search, is that it is only asymptotically optimal, and for most problems we care today the $$O(1)$$ constant is very important

http://www.scholarpedia.org/article/Universal_search

If there exists a program $p\ ,$ of length $l(p)\ ,$ that can solve the problem in time $\text{time}(p)\ ,$ then Universal Search will solve the problem in a time $2^{l(p)+1}\text{time}(p)$ at most. This exponential growth of computational cost in the algorithmic complexity $l(p)$ of the fastest solver makes practical applications of Universal Search problematic: nonetheless, the algorithm has inspired various others, including Hutter Search, the Optimal Ordered Problem Solver (OOPS) and the Gödel Machine.