Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

Computational systems biology aims at integrating biology and computational methods to
gain a better understating of biological phenomena. It often requires the assistance of global …

Very recently, the first mathematical runtime analyses for the NSGA-II, the most common
multi-objective evolutionary algorithm, have been conducted. Continuing this research …

Computational time complexity analyzes of evolutionary algorithms (EAs) have been
performed since the mid-nineties. The first results were related to very simple algorithms …

Abstract We study the (1, λ)-EA with mutation rate c/n for c≤ 1, where the population size is
adaptively controlled with the (1: s+ 1)-success rule. Recently, Hevia Fajardo and Sudholt …

The exploration vs exploitation dilemma is to balance exploring new but potentially less fit
regions of the fitness landscape while also focusing on regions near the fittest individuals …

Abstract Co-evolutionary algorithms (CoEAs), which pair candidate designs with test cases,
are frequently used in adversarial optimisation, particularly for binary test-based problems …

Local search is the most basic strategy in optimization settings when no specific problem
knowledge is employed. While this strategy finds good solutions for certain optimization …

Many real-world applications have the time-linkage property, and the only theoretical
analysis is recently given by Zheng, et al.(TEVC 2021) on their proposed time-linkage …

Real-world optimisation problems often involve dynamics, where objective functions may
change over time. Previous studies have shown that evolutionary algorithms (EAs) can solve …