The average convergence rate (ACR) measures how fast the approximation error of an
evolutionary algorithm converges to zero per generation. It is defined as the geometric …

Evolutionary algorithms (EAs) are random optimization methods inspired by genetics and
natural selection, resembling simulated annealing. We develop a method that can be used …

Surrogate-assisted evolutionary algorithms have been widely utilized in science and
engineering fields, while rare theoretical results were reported on how surrogates influence …

Runtime analysis of continuous evolutionary algorithms (EAs) is a hard topic in the
theoretical research of evolutionary computation, relatively few results have been obtained …

For complicated problems that cannot be solved in polynomial first hitting time (FHT)/running
time (RT), a remedy is to perform approximate FHT/TH analysis for given approximation …

There have been many studies on the runtime analysis of evolutionary algorithms in discrete
optimization, and however, relatively few homologous results have been obtained on …

When evolutionary algorithms (EAs) are unlikely to locate precise global optimal solutions
with satisfactory performances, it is important to substitute alternative theoretical routine for …

Drift analysis is a useful tool for estimating the running time of evolutionary algorithms. A
new representation of drift analysis, called average drift analysis, is described in this paper …

Although there have been many studies on the runtime of evolutionary algorithms in discrete
optimization, relatively few theoretical results have been proposed on continuous …

Performance of differential evolution, which is one of the most competitive evolutionary
algorithms, heavily depends on the utilization of feedback information. The feedback …