How computer generate random number?

Computers have become an indispensable part of our daily lives, capable of performing complex tasks at lightning-fast speeds. One of the intriguing aspects of computer technology is its ability to generate random numbers. But how do computers accomplish this feat? In this article, we will explore the mechanisms behind the generation of random numbers in computers and shed light on this fascinating process.

Understanding Randomness

Before delving into the specifics of how computers generate random numbers, it is crucial to comprehend what randomness truly means. In the context of computing, randomness refers to the absence of predictability. In other words, a random sequence of numbers does not follow any pattern or exhibit any discernible order. This unpredictability is essential for various applications like encryption, gambling simulations, scientific experiments, and statistical sampling.

The Pseudorandom Number Generators (PRNGs)

**How do computers generate random numbers?** Contrary to what one might think, computers do not generate true random numbers. Instead, they employ algorithms known as Pseudorandom Number Generators (PRNGs) to simulate randomness. These algorithms use mathematical formulas and seed values to generate long sequences of numbers that approximate randomness. The seed value serves as the initial input for the algorithm, and from there, the PRNG generates a chain of numbers based on predefined rules.

Pseudorandom numbers may seem random, but they are deterministic in nature. Given the same seed value, a PRNG will always produce the same sequence of numbers. Therefore, to introduce some level of randomness, the seed value is often based on factors like the current time, system events, or user interactions.


1. How can computers generate random numbers if they are deterministic?

Computers use Pseudorandom Number Generators (PRNGs) that produce sequences of numbers that approximate randomness but are actually deterministic.

2. Is there any way to make computers generate true random numbers?

Yes, hardware-based random number generators (HRNGs) can generate true random numbers by utilizing physical phenomena like radioactive decay or electronic noise.

3. Why do computers use PRNGs instead of true random numbers?

PRNGs are computationally efficient and produce long sequences of random-like numbers, making them suitable for most applications. HRNGs, on the other hand, are slower and may not always be available in all computing environments.

4. Which PRNG algorithms are commonly used in computers?

Popular PRNG algorithms include Linear Congruential Generators (LCGs), Mersenne Twister, and XORShift.

5. Can PRNGs be used for cryptographic purposes?

While PRNGs can generate random-like sequences, they are not suitable for cryptographic purposes. Cryptographically Secure Pseudorandom Number Generators (CSPRNGs) are specifically designed for such applications.

6. What are the drawbacks of using PRNGs?

PRNGs can exhibit statistical patterns if not properly designed or if the seed values are not truly random. This predictability can be exploited by malicious entities.

7. Are PRNGs predictable if the seed values are known?

Yes, if the seed values used in a PRNG are known, the entire sequence of numbers generated can be reproduced. Hence, it is important to protect the seed values from unauthorized access.

8. How do PRNGs ensure that the generated numbers are evenly distributed?

PRNG algorithms are designed to distribute the generated numbers uniformly over a specified range to avoid biases and create a more unbiased randomness appearance.

9. Can computers repeat the same random number during subsequent executions?

Yes, if the same seed value is used, computers can repeat the same sequence of random numbers.

10. Do different computer systems produce the same random numbers using PRNGs?

Yes, as long as the same seed value is used, PRNG algorithms will produce the same sequence of random numbers across different computer systems.

11. Can developers influence the randomness of PRNGs?

Developers can influence the randomness of PRNGs by controlling the choice of seed values and selecting appropriate PRNG algorithms for their specific applications.

12. How can one improve the randomness of PRNG sequences?

By using longer PRNG periods, combining multiple PRNG algorithms, or incorporating external entropy sources, the randomness of PRNG sequences can be improved.

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