Random Number Generators

Random number generation

Random number generation is a process where, most often through the random number generator (RNG) or a random sequence of symbols or numbers that cannot be reasonably predictable better than random chance is generated. This means that the resultant sequence may contain patterns that are detectable in hindsight however unpredictable to the foresight. In reality, random number generators can be hardware random-number generators(HRNGS) that generate random numbers, and each generation is dependent on the current value of the physical attribute that is changing constantly in a way that's difficult to predict. This would be in contrast to so-called "random number generations" done by pseudorandom number generators (PRNGs) that generate numbers that only look random but are in fact pre-determined--these generations can be reproduced simply by knowing the state of the PRNG.

The many applications of randomness have led to the invention of several different methods for generating random data. Some of them have been around in the past since the beginning of the era, among them are known "classic" examples, including the rolling of dice, coin flipping, the shuffling of playing cards and the use of yarrow-stalks (for divination) inside the I Ching, as well as countless other methods. Because of them being mechanical methods the creation of large quantities of numbers that were sufficiently random (important in the field of statistics) required much work and effort. Thus, results would sometimes be collated and distributed as random number tables.

Many computational methods to generate pseudorandom numbers are in use. However, none of them meet the objective of truly randomness, though they do have a chance to pass, but with varying degrees of success, some of the tests of randomness that measure how unpredictable their results are (that is in what extent their patterns can be discerned). They are generally not suitable for various applications, such as Cryptography. However, carefully-designed Cryptographically safe pseudorandom numbers generators (CSPRNGS) are also exist, and have features specifically designed for use in cryptography.

Practical applications and uses[edit]

Original article The applications for randomness

Random number generators have applications in gambling, statistical sampling and computer simulation, cryptography, fully randomized design as well as other areas in which an unpredictable outcome is desired. Most often, in cases that require an element of uncertainty as the primary characteristic such as security, hardware generators tend to be preferred over pseudorandom algorithms, where they are feasible.

Pseudorandom number generators are very efficient in developing Monte-Carlo simulations, since debugging is made easier by having the capability of running the identical set of random number sequences repeatedly using that same random seed. They can also be used for cryptography, so long as it is ensured that the seed is kept secret. Both receivers and senders can generate the same set of numbers automatically for use as keys.

The creation of pseudorandom numbers is a critical and routine part of computer programming. While cryptography and certain algorithms rely on a significant amount in obvious randomness, other tasks require only a modest amount of unpredictability. Simple examples include giving a user an "random quote of the day" or determining the way an adversary controlled by computers could play in a computer-controlled game. More shrewd forms of randomness are utilized in hash algorithms, as well as in the creation of amortized search and sorting methods.

Some applications which appear at first sight to be suitable to be suitable for randomization are in fact not all that simple. For instance, a system which "randomly" selects music tracks for a background music system must only have the appearance of random, and may even allow for control over the music selection the system has no limit on having the same track appear several times.