1 code implementation • 9 Sep 2022 • Nina Bulanova, Arina Buzdalova, Carola Doerr
In this work, we first show that the re-optimization approach suggested by Doerr et al. reaches a limit when the problem instances are prone to more frequent changes.
no code implementations • 23 Feb 2021 • Kirill Antonov, Maxim Buzdalov, Arina Buzdalova, Carola Doerr
With the goal to provide absolute lower bounds for the best possible running times that can be achieved by $(1+\lambda)$-type search heuristics on common benchmark problems, we recently suggested a dynamic programming approach that computes optimal expected running times and the regret values inferred when deviating from the optimal parameter choice.
no code implementations • 19 Jun 2020 • Arina Buzdalova, Carola Doerr, Anna Rodionova
We demonstrate that our HQL mechanism achieves equal or superior performance to all techniques tested in [Rodionova et al., GECCO'19] and this -- in contrast to previous parameter control methods -- simultaneously for all offspring population sizes $\lambda$.
no code implementations • 17 Apr 2019 • Anna Rodionova, Kirill Antonov, Arina Buzdalova, Carola Doerr
We observe that for the 2-rate EA and the EA with multiplicative update rules the more generous bound $p_{\min}=1/n^2$ gives better results than $p_{\min}=1/n$ when $\lambda$ is small.
no code implementations • 24 Apr 2017 • Irina Petrova, Arina Buzdalova
In addition, we present theoretical analysis of the proposed modification on the XdivK problem.
no code implementations • 22 Mar 2016 • Arkady Rost, Irina Petrova, Arina Buzdalova
Online parameter controllers for evolutionary algorithms adjust values of parameters during the run of an evolutionary algorithm.