can only improve a deterministic algorithm if the deterministic algorithm was poorly designed in the first place. Fred W. Glover argues that reliance on random elements may prevent the development of more intelligent and better deterministic components. The way in which results of stochastic optimization algorithms are usually presented (e.g., presenting only the average, or even the best, out of N runs without any mention of the spread), may also result in a positive bias towards randomness. See also Global optimization Machine learning Scenario optimization Gaussian process State Space Model Model predictive control Nonlinear programming Entropic value at risk References Further reading Michalewicz, Z. and Fogel, D. B. (2000), How to Solve It: Modern Heuristics, Springer-Verlag, New York. External links COSP Monte Carlo methods