Roulette-wheel selection via stochastic acceptance

Roulette-wheel selection is a frequently used method in genetic or evolutionary algorithms. Existing routines select one of N individuals using search algorithms of O(N) or O(\log N) complexity. We present a simple roulette-wheel selection algorithm that typically has O(1) complexity and is based on stochastic acceptance instead of searching. With minor modifications the algorithm might be also used for sampling with fitness being cut-off at a certain value or for sampling without replacement.