6533b857fe1ef96bd12b4e1a
RESEARCH PRODUCT
On sampling error in evolutionary algorithms
Dirk SchweimDavid WittenbergFranz Rothlaufsubject
education.field_of_studyDistribution (mathematics)Population sizePopulationStatisticsEvolutionary algorithmInitializationSmall population sizeGenetic programmingeducationConfidence intervalMathematicsdescription
The initial population in evolutionary algorithms (EAs) should form a representative sample of all possible solutions (the search space). While large populations accurately approximate the distribution of possible solutions, small populations tend to incorporate a sampling error. A low sampling error at initialization is necessary (but not sufficient) for a reliable search since a low sampling error reduces the overall random variations in a random sample. For this reason, we have recently presented a model to determine a minimum initial population size so that the sampling error is lower than a threshold, given a confidence level. Our model allows practitioners of, for example, genetic programming (GP) and other EA variants to estimate a reasonable initial population size.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-07-07 | Proceedings of the Genetic and Evolutionary Computation Conference Companion |