Random Number Generators

Random number generation

Random number generation is a process in which, generally using an random number generator (RNG) or a random sequence of symbols or numbers that can't be reliably anticipated better than by random chance is generated. This means that the particular outcome sequence will contain some patterns that are recognizable in hindsight yet unpredictable to foresight. Truly random number generators can be hardware random-number generators(HRNGS) that produce random numbers. Each generation is dependent on the present value of a physical environment's attribute , which is constantly changing and in a way nearly impossible to comprehend. 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.

Many applications of randomness have led to the creation of a variety of methods for producing random data. Some of these have existed since antiquity, there are many famous "classic" examples, including the rolling of dice, coin flipping, the shuffling and shuffle of playing cards, and the use of yarrow-stalks (for predictions) inside the I Ching, as well as a myriad of other techniques. Because of their mechanical character of techniques making large numbers of randomly generated numbers (important in the field of statistics) required lots of work and time. This meant that the results might be collected and distributed as random number tables.

Several computational methods that generate pseudorandom numbers exist. None of them achieve the notion of real randomness. They may achieve, with various degrees of success, some of the tests that are designed to determine the uncertainty of their results (that is how much their patterns are discernible). They are generally not suitable for use in applications like cryptography. However, well-designed Cryptographically safe pseudorandom numbers generators (CSPRNGS) are also are available, featuring features designed specifically for use in cryptography.

Practical applications and uses[edit]

The main article is: The applications in randomness

Random number generators are utilized to gaming, statistical sampling and computer simulation, cryptography, totally random design and many other areas where a random outcome is desirable. Most often, in cases that require unpredictable results as their primary attribute such as security applications, hardware generators are preferred over pseudorandom algorithm, if it is feasible.

Pseudorandom numbers generators are extremely efficient in developing Monte Carlo simulations, as testing can be made easier with being able to run the same number of random sequences repeatedly using exactly the same random seed. They are also used in cryptography , as long that you keep the seed remains secret. The receiver and the sender can create the same set of numbers in a way to use as keys.

The generation of pseudorandom numbers is a critical and routine part of computer programming. While cryptography and certain mathematical algorithms require a significant amount in visible randomness, other tasks require only some degree of uncertainty. Examples of this could be presenting a user with the "random quote of the day", or determining which direction a computer-controlled enemy could move in a computer gaming. More shrewd forms of randomness are used in hash algorithms and in the development of amortized searching and sorting algorithms.

Certain applications that appear at first glance to be suited to be suitable for randomization are in fact not quite so simple. For instance, a program that "randomly" selects music tracks for a background music system must only look random. It might even allow for control over the music selection A true random system has no limit on having the same track appear twice or three times.