# Random number generators

**DOI:**https://doi.org/10.1007/1-4020-0611-X_852

## INTRODUCTION

Several algorithms and heuristics in operations research require a source of random numbers. Such numbers are needed, for example, for Monte Carlo integration, stochastic discrete-event simulation, and probabilistic algorithms (like genetic algorithms or simulated annealing). Typically, the so-called “random numbers” are produced by a deterministic computer program, and are therefore not random at all. The aim of such a program, called a *random number generator*, is to produce a sequence of values which “look” as if they were a typical sample of i.i.d. (independent and identically distributed) random variables, say from the *U*(0,1) distribution (the uniform distribution between 0 and 1). Some generators may also produce random integers, or random bits, etc. Since the sequence produced is really deterministic, it is often called a *pseudorandom sequence* and the generator producing it is then called a *pseudorandom number generator*. Here, we adopt the well-accepted practice of...

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