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

For large space dimensions, the log-linear convergence of the elitist evolution strategy with
a 1/5 success rule on the sphere fitness function has been observed, experimentally, from …

Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are
probabilistic algorithms for optimization, which mimic operators from natural selection and …

This work introduces the Unscented Genetic Algorithm (U-GA), which combines ideas from
evolutionary computation and Kalman filters to devise a novel approach to solve GA-hard …

Previous research proposed the uniform mutation inside the sphere as a new mutation
operator for evolution strategies (continuous evolutionary algorithms), with a case study of …

Performance of evolutionary algorithms in real space is evaluated by local measures such
as success probability and expected progress. In high-dimensional landscapes, most …

The brain storm optimization (BSO) algorithm has received increased attention in the field of
evolutionary computation. While BSO has been applied in numerous industrial scenarios …

The pigeon-inspired optimization (PIO) algorithm is a newly presented swarm intelligence
optimization algorithm inspired by the homing behavior of pigeons. Although PIO has …

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

摘要计算时间分析是进化算法理论基础研究中的重要课题, 也是一大难点. 该文基于停时理论,
结合时齐马氏过程的性质, 将进化算法的首达时间视为停时, 提出了分析进化算法首达时间的 …