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Quasi-random number

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A deterministically generated sample sequence designed to distribute points evenly across a space rather than mimic probabilistic randomness.

A quasi-random number belongs to a deterministic sequence designed to distribute points evenly across a space. Unlike pseudo-random numbers, it prioritizes coverage over an appearance of unpredictability. section 7.1

For population-based metaheuristics, quasi-random initialization gives high diversity at low computational cost, although its initial solutions generally have low quality. section 7.1

Generate a quasi-random population

With N = 200, ghalton.GeneralizedHalton(7, 23) creates a sequence, and sequencer.get(N) converts it into a NumPy population. Plotting the points makes its even spatial coverage visible.

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Defines the distinction and demonstrates Python generation, but does not derive the underlying sequence construction.
Examples
Uses a 200-point Generalized Halton population, scatter plots, and comparisons with pseudo-random, Sobol, and Latin Hypercube sampling.
What it assumes
Assumes a population-based optimization setting while distinguishing random, pseudo-random, and quasi-random sampling.
What you can do after
Apply it via ghalton to generate diverse initial populations with low computational cost.
How it connects
Connects quasi-random numbers with initial populations, exploration, exploitation, pseudo-random numbers, and heuristic initialization.
Cover of Optimization Algorithms: AI techniques for design, planning, and control problems
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.