Random Number Generators

Random number generation

Random number generation is a process where, most often through an random number generator (RNG) an array of numbers or symbols that can't be reliably predictable better than the random chance is created. This means that the particular resultant sequence may contain patterns that are detectable in hindsight however unpredictable to the foresight. It is true that random number generators can be hardware random-number generators(HRNGS) which generate random numbers. Each generation is a function of the current value of a physical environment's attribute , which is constantly changing in a manner that is practically impossible to model. 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.

Numerous applications of randomness have led to the invention of many different methods of generating random data. Certain of them have been in use in the past since the beginning of the era, there are many well-known "classic" examples, including the rolling of dice, coin flipping, the shuffling of cards, the usage of yarrow plants (for for divination) as part of the I Ching, as well as numerous other methods. Due to how mechanical these methods producing large amounts of random numbers (important in statistics) required much work and time. The results could be compiled and distributed in random number tables.

Several computational methods to generate pseudorandom numbers exist. Each of them fails to meet the notion of real randomness, but they can meet, with varying results, certain statistical tests for randomness designed to determine the uncertainty of their results (that is how often their patterns are discernible). This makes them ineffective for purposes such as the cryptographic field. However, thoughtfully designed Cryptographically safe pseudorandom numbers generators (CSPRNGS) also exist, with special functions specifically designed to be used in cryptography.

Practical applications and uses[edit]

Main article: Application in randomness

Random number generators are utilized that are used in gambling, statistical sampling and computer simulation, cryptography fully randomized design, and other areas where a random outcome is desired. Generally, in applications having unpredictable outcomes as their primary feature including security, hardware generators tend to be preferred over pseudorandom algorithms, when feasible.

Pseudorandom generation tools are beneficial in the development of Monte Carlo methods of simulations, since testing can be made easier with being able to run the same set of random number sequences over and over again with the same random seed. They are also used in cryptography - so long that they are secret. seed is kept secret. Both the receiver and sender can generate the same set of numbers in a way to use as keys.

The generation of pseudorandom numbers is a vital and regular task in computer programming. While cryptography and certain mathematical algorithms require a large amount of perceived randomness, many other applications require the slightest amount of uncertainty. One of the most common examples is giving a user the "random quote of the day", or determining which direction a computer-controlled enemy could be moving in a computer game. The less random forms of randomness are employed in hash algorithms and in creating amortized searching or sorting techniques.

Some programs that appear on first glance to be suitable for randomization actually aren't quite so simple. For instance, a system which "randomly" selects music tracks for a background music system will only have the appearance of random and could even be able to control the choice of music the system is not restricted to the same thing appearing two or three times.

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