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Genetic Algorithm Population Calculator

Work out genetic algorithm population instantly with clear inputs, formula shown and shareable results.

About Genetic Algorithm Population Calculator

Genetic algorithm population is conventionally scaled with chromosome length so the initial population samples every gene position several times; a multiplier of two to ten times the bit length is typical. Total cost is population times generations fitness evaluations, which is the number that matters when each evaluation is a simulation or a model training run. Mutation rate is usually set near 1/length so about one bit flips per individual.

Formula

GA sizing

population = length x multiplier; evaluations = population x generations; expected mutations = population x length x mutation rate; search space = 2^length

Worked example

With the default values below, Genetic Algorithm Population Calculator returns Recommended population size of 160. Change any input above and every figure here is recalculated in your browser.

Inputs used

Chromosome length in bitsaccepts 1–2,000
40
Population size per bitaccepts 0.1–50
4
Generationsminimum 1
200
Mutation rate per bitaccepts 0–1
0.02

Results

Recommended population size
160
Fitness evaluations required
32,000
Expected bit mutations per generation
128.0
Size of the search space
1,099,511,627,776

Step by step

  1. 1Values used
  2. 2GA sizing
  3. 3Recommended population size= 160
  4. 4Fitness evaluations required= 32,000
  5. 5Expected bit mutations per generation= 128.0
  6. 6Size of the search space= 1,099,511,627,776

Assumptions and limits

  • The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 1 decimal place, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

What mutation rate should I use?

Around 1 divided by chromosome length, so roughly one bit changes per offspring. Much higher and the search becomes random; much lower and it stops escaping local optima.

How do I know when to stop?

Track best and mean fitness. When the gap closes and the best stops improving for many generations, the population has converged and further generations waste evaluations.

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