- author = "Dirk Schweim and David Wittenberg and Franz Rothlauf",
- title = "On Sampling Error in Evolutionary Algorithms",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference",
- year = "2021",
- editor = "Carola Doerr",
- pages = "43--44",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Theory of computation, Design and analysis of algorithms, Sampling Error, Initial Supply, Evolutionary Algorithms, Building Blocks, Initial Population,n-Grams",
- isbn13 = "978-1-4503-8351-6",
-
DOI = "
doi:10.1145/3449726.3462726",
- size = "2 pages",
- abstract = "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.",
-
notes = "Short version of \cite{Schweim:NatComput}
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
