Domino convergence
Carsten Witt · Proceedings of the Genetic and Evolutionary Computation Conference · 2018
In the theory community of evolutionary computation, linear pseudo-boolean functions are often regarded as easy problems since all of them can be optimized in expected time O(n log n) by simple unbiased algorithms. However, results from genetic algorithms and estimation-of-distribution algorithms indicate that these algorithms treat different linear functions differently. More precisely, an effect called "domino convergence" is described in the literature, which means that bits of large weight in the linear function are optimized earlier than bits of low weight. Hence, different linear functions may lead to rather different expected optimization times.