Abstract: A new adaptive restart rule for Genetic Algorithms (GAs) is considered. The rule is based on the Schnabel Census method, originally developed for statistical estimation of a size of animal population. In this paper, the Schnabel Census method is applied as a heuristic to estimate the number of different solutions that may be visited with positive probability, given the current distribution of offspring. The rule consists in restarting a GA as soon as the maximum likelihood estimate reaches the number of different solutions observed at the recent iterations. We demonstrate how the new restart rule can be applied in a GA with steady-state replacement scheme, using three different combinatorial optimization problems as examples. Computational experiments on well-known benchmarks show a statistically significant advantage of the GAs with the new restarting rule over the original versions of GAs. The new restart rule also tends to be superior to the well-known rule, which restarts an algorithm when the current iteration number is twice the number of iterations till the current best incumbent was found.

Cite: Eremeev A.V. , Zakharova Y.V.
A restart rule for genetic algorithms based on Schnabel census
Сибирские электронные математические известия (Siberian Electronic Mathematical Reports). 2026. V.23. N1. P.554-570. DOI: 10.33048/semi.2026.23.034 WOS Scopus

Dates:

Submitted:	Aug 30, 2025
Published online:	Jun 5, 2026

Identifiers:

≡ Web of science:	WOS:001836362500033
≡ Scopus:	2-s2.0-105041537139

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