---
res:
  bibo_abstract:
  - 'Most runtime analyses of randomised search heuristics focus on the expected number
    of function evaluations to find a unique global optimum. We ask a fundamental
    question: if additional search points are declared optimal, or declared as desirable
    target points, do these additional optima speed up evolutionary algorithms? More
    formally, we analyse the expected hitting time of a target set OPT{$\cup$}S where
    S is a set of non-optimal search points and OPT is the set of optima and compare
    it to the expected hitting time of OPT. We show that the answer to our question
    depends on the number and placement of search points in S. For all black-box algorithms
    and all fitness functions with polynomial expected optimisation times we show
    that, if additional optima are placed randomly, even an exponential number of
    optima has a negligible effect on the expected optimisation time. Considering
    Hamming balls around all global optima gives an easier target for some algorithms
    and functions and can shift the phase transition with respect to offspring population
    sizes in the (1,{$\lambda$}) EA on OneMax. However, for the one-dimensional Ising
    model the time to reach Hamming balls of radius (1/2-{$ϵ$})n around optima does
    not reduce the asymptotic expected optimisation time in the worst case. Finally,
    on functions where search trajectories typically join in a single search point,
    turning one search point into an optimum drastically reduces the expected optimisation
    time.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Jakob
      foaf_name: Bossek, Jakob
      foaf_surname: Bossek
      foaf_workInfoHomepage: http://www.librecat.org/personId=102979
    orcid: 0000-0002-4121-4668
  - foaf_Person:
      foaf_givenName: Dirk
      foaf_name: Sudholt, Dirk
      foaf_surname: Sudholt
  bibo_doi: 10.1016/j.tcs.2023.113757
  dct_date: 2023^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0304-3975
  dct_language: eng
  dct_subject:
  - Evolutionary algorithms
  - pseudo-Boolean functions
  - runtime analysis
  dct_title: Do Additional Target Points Speed Up Evolutionary Algorithms?@
...