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Different models can be generated by changing both deterministic parameters and a random seed. In computing , procedural generation is a method of creating data algorithmically as opposed to manually, typically through a combination of human-generated content and algorithms coupled with computer-generated randomness and processing power.
In video games using procedural world generation, the map seed is a (relatively) short number or text string which is used to procedurally create the game world ("map"). "). This means that while the seed-unique generated map may be many megabytes in size (often generated incrementally and virtually unlimited in potential size), it is possible to reset to the unmodified map, or the unmodified ...
A SWB generator is the basis for the RANLUX generator, [19] widely used e.g. for particle physics simulations. Maximally periodic reciprocals: 1992 R. A. J. Matthews [20] A method with roots in number theory, although never used in practical applications. KISS: 1993 G. Marsaglia [21] Prototypical example of a combination generator. Multiply ...
A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator. A pseudorandom number generator's number sequence is completely determined by the seed: thus, if a pseudorandom number generator is later reinitialized with the same seed, it will produce the same sequence of numbers.
The Lehmer random number generator [1] (named after D. H. Lehmer), sometimes also referred to as the Park–Miller random number generator (after Stephen K. Park and Keith W. Miller), is a type of linear congruential generator (LCG) that operates in multiplicative group of integers modulo n. The general formula is
An astrophysical Monte Carlo simulator examined the time to generate 10 7 64-bit random numbers using RDRAND on a quad-core Intel i7-3740 QM processor. They found that a C implementation of RDRAND ran about 2× slower than the default random number generator in C, and about 20× slower than the Mersenne Twister.
A structure similar to LCGs, but not equivalent, is the multiple-recursive generator: X n = (a 1 X n−1 + a 2 X n−2 + ··· + a k X n−k) mod m for k ≥ 2. With a prime modulus, this can generate periods up to m k −1, so is a useful extension of the LCG structure to larger periods.
In theoretical computer science and cryptography, a pseudorandom generator (PRG) for a class of statistical tests is a deterministic procedure that maps a random seed to a longer pseudorandom string such that no statistical test in the class can distinguish between the output of the generator and the uniform distribution.