RandomSeed

Randomseed, typically called a seed, initializes the state of a pseudorandom number generator (PRNG). Most RNGs are deterministic: given the same internal state, they produce the same sequence of numbers. The seed provides the initial state from which that sequence is derived. In many languages, the function or procedure is named seed, srand, or RandomSeed.

The primary purpose of a seed is to enable reproducibility. By using a fixed seed, researchers can reproduce results in simulations, tests, and analyses. Varying the seed yields different sequences, which is useful for exploring different hypothetical scenarios or for avoiding repeated patterns in stochastic processes.

Mechanically, a seed is passed to the RNG as an input value, often an integer or an array. The RNG then uses an algorithm (such as an LCG, Mersenne Twister, PCG, or others) to transform that seed into a long internal state and then generate numbers from that state. Changing the seed changes the entire output sequence. Some libraries support reseeding mid-run or creating multiple independent streams of numbers; some RNGs maintain separate seed states for these streams.

Practical considerations include choosing a seed appropriate to the task. For non-secure, conventional uses, a time-based seed or a explicitly provided constant is common to balance reproducibility and variability. It is good practice to log the seed value for later replication. For cryptographic or security-sensitive tasks, use a cryptographically secure RNG and a high-entropy seed from a suitable source (for example, a system entropy source). In parallel or multi-threaded contexts, ensure independent seeds to avoid unwanted correlations.

Examples: In Python, random.seed(x); in C, srand(x); in Lua, math.randomseed(x); in MATLAB, rng(x).