On the investigation of population sizing of genetic algorithms using optimal mixing
Journal
GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference
Pages
820-828
Date Issued
2019
Author(s)
Abstract
Genetic algorithms using optimal mixing have shown promising results, while lack of theoretical supports. This paper investigates population sizing from the supply aspect under the optimal mixing scenario. Specifically, more precise analyses on supply, including the expectation and the lower bound, are made. In addition, considering recombining one randomly generated chromosome with the rest of the population to achieve the global optimum, the tight bounds of the size of the population providing proper fragments chosen by restricted oracles are derived. Tight bounds on problems with ring topologies where a subfunction overlaps two other subfunctions are also derived. Finally, experiments are conducted and well match the derivations.
Type
conference paper