Random Optimization - Algorithm

Algorithm

Let f: ℝn → ℝ be the fitness or cost function which must be minimized. Let x ∈ ℝn designate a position or candidate solution in the search-space. The basic RO algorithm can then be described as:

• Initialize x with a random position in the search-space.
• Until a termination criterion is met (e.g. number of iterations performed, or adequate fitness reached), repeat the following:
  • Sample a new position y by adding a normally distributed random vector to the current position x
  • If (f(y) < f(x)) then move to the new position by setting x = y
• Now x holds the best-found position.