Create REFERENCES.bib also for R.

Author: MLopez-Ibanez

bibkeys.txt

@@ -1 +1,2 @@
 LopPaqStu09emaa
+LopVerDreDoe2025

python/doc/source/REFERENCES.bib

@@ -1,6 +1,36 @@
 % DO NOT EDIT THIS FILE. It is auto-generated by "update_bib.sh".
 @preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {Ant} {System}} } # {\providecommand{\rpackage}[1]{{#1}} } # {\providecommand{\softwarepackage}[1]{{#1}} } # {\providecommand{\proglang}[1]{{#1}} } # {\providecommand{\BIBdepartment}[1]{{#1}, } }}
 
+@article{LopVerDreDoe2025,
+  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Diederick Vermetten  and  Johann Dreo  and  Carola Doerr },
+  title = {Using the Empirical Attainment Function for Analyzing
+                  Single-objective Black-box Optimization Algorithms},
+  journal = {IEEE Transactions on Evolutionary Computation},
+  year = 2025,
+  annote = {Pre-print: \url{https://doi.org/10.48550/arXiv.2404.02031}},
+  doi = {10.1109/TEVC.2024.3462758},
+  abstract = {A widely accepted way to assess the performance of iterative
+                  black-box optimizers is to analyze their empirical cumulative
+                  distribution function (ECDF) of pre-defined quality targets
+                  achieved not later than a given runtime. In this work, we
+                  consider an alternative approach, based on the empirical
+                  attainment function (EAF) and we show that the target-based
+                  ECDF is an approximation of the EAF. We argue that the EAF
+                  has several advantages over the target-based ECDF. In
+                  particular, it does not require defining a priori quality
+                  targets per function, captures performance differences more
+                  precisely, and enables the use of additional summary
+                  statistics that enrich the analysis. We also show that the
+                  average area over the convergence curves is a
+                  simpler-to-calculate, but equivalent, measure of anytime
+                  performance. To facilitate the accessibility of the EAF, we
+                  integrate a module to compute it into the IOHanalyzer
+                  platform. Finally, we illustrate the use of the EAF via
+                  synthetic examples and via the data available for the BBOB
+                  suite.},
+  keywords = {EAF-based ECDF}
+}
+
 @incollection{LopPaqStu09emaa,
   editor = { Thomas Bartz-Beielstein  and  Marco Chiarandini  and  Lu{\'i}s Paquete  and  Mike Preuss },
   year = 2010,

r/inst/REFERENCES.bib

@@ -0,0 +1,62 @@
+% DO NOT EDIT THIS FILE. It is auto-generated by "update_bib.sh".
+@preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {Ant} {System}} } # {\providecommand{\rpackage}[1]{{#1}} } # {\providecommand{\softwarepackage}[1]{{#1}} } # {\providecommand{\proglang}[1]{{#1}} } # {\providecommand{\BIBdepartment}[1]{{#1}, } }}
+
+@article{LopVerDreDoe2025,
+  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Diederick Vermetten  and  Johann Dreo  and  Carola Doerr },
+  title = {Using the Empirical Attainment Function for Analyzing
+                  Single-objective Black-box Optimization Algorithms},
+  journal = {IEEE Transactions on Evolutionary Computation},
+  year = 2025,
+  annote = {Pre-print: \url{https://doi.org/10.48550/arXiv.2404.02031}},
+  doi = {10.1109/TEVC.2024.3462758},
+  abstract = {A widely accepted way to assess the performance of iterative
+                  black-box optimizers is to analyze their empirical cumulative
+                  distribution function (ECDF) of pre-defined quality targets
+                  achieved not later than a given runtime. In this work, we
+                  consider an alternative approach, based on the empirical
+                  attainment function (EAF) and we show that the target-based
+                  ECDF is an approximation of the EAF. We argue that the EAF
+                  has several advantages over the target-based ECDF. In
+                  particular, it does not require defining a priori quality
+                  targets per function, captures performance differences more
+                  precisely, and enables the use of additional summary
+                  statistics that enrich the analysis. We also show that the
+                  average area over the convergence curves is a
+                  simpler-to-calculate, but equivalent, measure of anytime
+                  performance. To facilitate the accessibility of the EAF, we
+                  integrate a module to compute it into the IOHanalyzer
+                  platform. Finally, we illustrate the use of the EAF via
+                  synthetic examples and via the data available for the BBOB
+                  suite.},
+  keywords = {EAF-based ECDF}
+}
+
+@incollection{LopPaqStu09emaa,
+  editor = { Thomas Bartz-Beielstein  and  Marco Chiarandini  and  Lu{\'i}s Paquete  and  Mike Preuss },
+  year = 2010,
+  address = {Berlin~/ Heidelberg},
+  publisher = {Springer},
+  booktitle = {Experimental Methods for the Analysis of
+                  Optimization Algorithms},
+  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
+  title = {Exploratory Analysis of Stochastic Local Search
+                  Algorithms in Biobjective Optimization},
+  pages = {209--222},
+  doi = {10.1007/978-3-642-02538-9_9},
+  abstract = {This chapter introduces two Perl programs that
+                  implement graphical tools for exploring the
+                  performance of stochastic local search algorithms
+                  for biobjective optimization problems. These tools
+                  are based on the concept of the empirical attainment
+                  function (EAF), which describes the probabilistic
+                  distribution of the outcomes obtained by a
+                  stochastic algorithm in the objective space. In
+                  particular, we consider the visualization of
+                  attainment surfaces and differences between the
+                  first-order EAFs of the outcomes of two
+                  algorithms. This visualization allows us to identify
+                  certain algorithmic behaviors in a graphical way.
+                  We explain the use of these visualization tools and
+                  illustrate them with examples arising from
+                  practice.}
+}

update_bib.sh

@@ -16,6 +16,6 @@ sed -i 's#\\hspace{0pt}#{}{}{}#g' $tmpbib
 # Work around broken URL:
 sed -i 's%researchrepository.napier.ac.uk/id/eprint/3044%lopez-ibanez.eu/publications#LopezIbanezPhD%g' $tmpbib
 comment='% DO NOT EDIT THIS FILE. It is auto-generated by "update_bib.sh".'
-#echo $comment | cat --squeeze-blank - $tmpbib > r/inst/REFERENCES.bib
+echo $comment | cat --squeeze-blank - $tmpbib > r/inst/REFERENCES.bib
 echo $comment | cat --squeeze-blank - $tmpbib > python/doc/source/REFERENCES.bib
 rm -f $BIBFILES $tmpbib