Random Generator Algorithms 

Users could choose a random algorithm when they generate tickets. To choose an algorithm, when you click on the Generate Tickets button, on the popup dialog you can select the Random Generator Algorithm from the dropdown list. The default algorithm is "Basic".

Please find more information for each algorithm as follows:

About Algorithms

Here is a brief definition of each algorithm. For those who are interested in more details please lookup the algorithm online. 

Basic

The basic algorithm is developed using "Xorshift32", which is a type of pseudorandom number generator (PRNG) that uses bitwise XOR and bit-shifting operations to generate random numbers. Xorshift32 is known for being relatively simple and having good statistical properties, making it suitable for various applications that require pseudorandom numbers. 

AdtvLagdFiboGen(ALFG)

(Advanced Lagged Fibonacci Generator or ALFG) is a pseudorandom number generator known for its balance of randomness quality and computational efficiency. It recursively combines earlier generated numbers to produce new pseudorandom values, making it useful for various applications, including scientific simulations and statistical modeling. ALFGs offer versatility, but their effectiveness depends on specific parameters and initial seed values. 

Cryptograph.SecurePRNGs

'Cryptograph.SecurePRNGs' refers to a collection of pseudorandom number generators designed to meet the highest standards of cryptographic security. These generators are specifically engineered to produce random numbers suitable for use in cryptography, where data security and unpredictability are critical. They are utilized in applications like secure communication, encryption, digital signatures, and secure key generation to safeguard sensitive information from unauthorized access or attacks. 

Lin.CongruentGen.

linear congruential generator (LCG) is a well-known pseudorandom number generator (PRNG) algorithm. It is a simple and widely used method for generating pseudorandom sequences of numbers. An LCG operates based on a linear congruential formula, which combines the previous value in the sequence with a multiplier and an increment to produce the next pseudorandom value. While LCGs are computationally efficient, they have limitations in terms of their randomness properties and are not suitable for all applications, particularly those requiring high-quality randomness or cryptographic security. 

MersenneTwister

The "Mersenne Twister" is a popular and widely used pseudorandom number generator (PRNG) algorithm. It is known for its long period, which means it can produce a vast number of pseudorandom values before repeating. The Mersenne Twister is favoured for its high-quality randomness and statistical properties, making it suitable for various applications, including simulations, games, cryptography, and scientific research. Its long period and excellent randomness characteristics have contributed to its widespread use in many software and programming environments. 

FourTapShiftReg.

A "Four-Tap Shift Register" is a type of shift register in digital electronics that has four stages or storage elements. A shift register is a sequential circuit that can delay or store binary data by shifting it through a series of stages. In the case of a "Four-Tap" shift register, it can hold and process four bits of binary data. 

Mult.WithCarry(MWC)

"Multiply-With-Carry," and it is a type of pseudorandom number generator (PRNG) algorithm. This algorithm generates pseudorandom numbers by repeatedly multiplying a previous value by a multiplier and adding a carry value. The carry value is updated with each iteration, and the result is used as the next pseudorandom number in the sequence.

MWC generators are known for their simplicity and efficiency, and they can produce pseudorandom sequences that are suitable for various applications. The choice of multipliers and carry values can impact the quality and statistical properties of the generated numbers. MWC generators are often used in situations where a balance between randomness quality and computational efficiency is required.

WellEqdis.LngPeriodLin(WELL)

"Well Equidistributed Long-Period Linear" or simply "WELL." The WELL algorithm is designed to provide a long period (i.e., a vast number of pseudorandom values before repeating) while maintaining good equidistribution properties, meaning that the generated numbers are evenly distributed and exhibit good statistical randomness.

WELL generators are often favored for applications that require high-quality pseudorandom numbers, such as scientific simulations, cryptography, and statistical modeling. They offer a balance between randomness quality and computational efficiency, making them suitable for various numerical and computational tasks. The specific details and parameters of the WELL algorithm may vary in different implementations.