OneMax is not the Easiest Function for Fitness Improvements

1 code implementation • 14 Apr 2022 • Marc Kaufmann, Maxime Larcher, Johannes Lengler, Xun Zou

In this paper we disprove this conjecture and show that OneMax is not the easiest fitness landscape with respect to finding improving steps.

Self-adjusting Population Sizes for the $(1, λ)$-EA on Monotone Functions

1 code implementation • 1 Apr 2022 • Marc Kaufmann, Maxime Larcher, Johannes Lengler, Xun Zou

Recently, Hevia Fajardo and Sudholt have shown that this setup with $c=1$ is efficient on \onemax for $s<1$, but inefficient if $s \ge 18$.

Molecular modeling with machine-learned universal potential functions

no code implementations • 6 Mar 2021 • Ke Liu, Zekun Ni, Zhenyu Zhou, Suocheng Tan, Xun Zou, Haoming Xing, Xiangyan Sun, Qi Han, Junqiu Wu, Jie Fan

Molecular modeling is an important topic in drug discovery.

Drug Discovery

Exponential Slowdown for Larger Populations: The $(μ+1)$-EA on Monotone Functions

no code implementations • 30 Jul 2019 • Johannes Lengler, Xun Zou

In particular, it was known that the $(1+1)$-EA and the $(1+\lambda)$-EA can optimize every monotone function in pseudolinear time if the mutation rate is $c/n$ for some $c<1$, but they need exponential time for some monotone functions for $c>2. 2$.

Evolutionary Algorithms