# Algorithms for searching in high-dimensional binary data spaces

Is there any algorithm that can learn/search efficiently the best sequence of 1's and 0's of length $n$ to fulfill certain performance? The search is performed in a high-dimensional binary data space. This means that the search space is of $2^n$ where $n$ usually is in the order of hundreds. I know that Discrete Particle Swarm Optimization, Simulated Annealing or Genetic Algorithms can be used for this purpose, but I'm afraid they tend to get stuck in local optima and/or can not work efficiently in high-dimensional spaces.

• without info about your specific problem, this is hard to answer. Care to elaborate? – Memming Dec 9 '16 at 5:11