Stochastic universal sampling

Q: How deeply does Optimization Algorithms: AI techniques for design, planning, and control problems cover stochastic universal sampling?

Explains the wheel, pointer spacing, number selected, and single-spin operation, but does not provide code or parameter tuning.

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

A selection strategy that uses one random roulette-wheel spin with evenly spaced pointers to select multiple individuals.

Stochastic universal sampling (SUS) selects multiple individuals for reproduction with one roulette-wheel spin and evenly spaced pointers. It helps reduce potential bias in roulette-wheel selection. section 7.3.3

The procedure is compact:

Place an outer roulette wheel around the fitness-proportional selection wheel. section 7.3.3
Set m evenly spaced pointers, where m is the number of individuals to select. section 7.3.3
Spin once and select every individual whose wheel segment contains a pointer. section 7.3.3

Ticket pricing example

A ticket-pricing population uses SUS with four selection points, selecting four individuals simultaneously from the roulette wheel.

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

Cover of Optimization Algorithms: AI techniques for design, planning, and control problems

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Related concepts

roulette-wheel selectionalternative to · Roulette-wheel selection is an evolutionary selection technique that assigns candidates probabilities proportional to their fitness, typically giving lower-fitness candidates nonzero probabilities.

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