Stochastic Universal Sampling is a method of weighted random sampling exhibiting less bias and spread that classic roulette wheel sampling. The intuition is a roulette wheel with n equally spaced steel balls spinning in unison around the wheel. This method has better properties and is more efficient that doing repeated samples from the wheel with or without replacement of the selected items.
Baker, James E. (1987). "Reducing Bias and Inefficiency in the Selection Algorithm". Proceedings of the Second International Conference on Genetic Algorithms and their Application (Hillsdale, New Jersey: L. Erlbaum Associates): 14–21.
Reference implementations on the web are scare, so here are a few:https://github.com/dwdyer/watchmaker/blob/master/framework/src/java/main/org/uncommons/watchmaker/framework/selection/StochasticUniversalSampling.java
See the SUSSelection.java buried in the latest tarball.