The runtime of such procedures is characterized by a random variable.
Different algorithms give rise to different probability distributions.
One can take advantage of such differences by combining several algorithms into a portfolio, and running them in parallel or interleaving them on a single processor. We provide a detailed evaluation of the portfolio approach on distributions of hard combinatorial search problems. We show under what conditions the portfolio approach can have a dramatic computational advantage over the best traditional methods. The first algorithm which finishes the search stops the others. Gomes, C. P., and Selman, B. 1997.