Random number generation is basically a system that, through a random number generator, produces a series of random numbers or symbols which cannot be specifically predicted better than by an equally intelligent random guess. In other words, it attempts to “foresee” an occurrence without actually knowing its precise nature or character. It is done by random number generators in various forms such as random number machines, lottery wheels, etc. While there are many different types of these generators, all have the same goal – to increase the amount of money won in random number games.
How does a generator to generate random numbers? The answer is that it doesn’t know and doesn’t care. What it knows is that there are many random integers N, which are arranged in a certain pattern, such as the Fibonacci numbers. By taking the successive strings of these numbers as they are formed, we can predict, with some accuracy, the next N random numbers that will come along.
There are many uses for this kind of random number generators. A good example would be a game of fortune, such as lotto. You can create a simulation of the future by randomly choosing digits and then connecting them together in a sequence that you think will occur. Another great use is in creating weather patterns. meteorologists use random number generators to generate a forecast of rain and clouds, and weather researchers use them to study the effects of climate on the probability of thunderstorms.
Now back to the original question: just what are random numbers? Well, true random numbers are completely random, i.e., there is no pattern, and thus we cannot say that any set of numbers is actually random. This is called ‘true randomness’. Applying this definition to random number generators, we get the following: the probability of a given number of random numbers being generated by a machine is equal to the product of its frequency with the uniform distribution
So now we know what randomness is, let’s talk about how it applies to programming. Often, in the implementation of programs, programmers must deal with large sets of random numbers, which they then must apply a series of rules or algorithms to generate the desired result. One common example would be how to determine the best possible layout for a software program. In such a situation, it is obvious that programmers would want to maximize the random number output. To do this, they implement strategies that ensure the highest probability of producing the desired outcome.
In a situation like this, programmers usually resort to pseudo Random Number Generators (PRNG). Pseudo-random number generators, as their name implies, take randomness from other sources and implement it into the computer. For instance, a seed, which is simply a random number, may be seed incremented by random number generators until it reaches a particular level. At that point, the seed will be made part of the official random number generator’s seed and thus make it more reliable in generating results that are desirable. xsmb
Another common application of a pseudo random number generator would be in the encryption algorithm. Encryption keys rely heavily on the strength of their key, thus making sure that the encryption algorithm is unpredictable. This is one of the most important benefits of using a pseudorandom number generator; the strength of the encryption key, in the long run, will be protected from attacks by hackers. Without the unpredictable random numbers that a PRNG can provide, an encryption key may become vulnerable to attacks. xsdl
The Convex Alternative Linear Congruential Generator is used mainly in the financial and banking industries. With this generator, random numbers are generated based on a mathematical equation. By changing the initial equation (the seed), a constant term can be altered which causes the generator to generate new random numbers that are congruent to the original equation. The changed terms, when combined with other factors such as the concentration of money in a certain region or country can create the desired result. This kind of generator can help achieve high levels of randomness and deterring hacking, hence its importance in the finance industry.