* articles.bib (LopVerDreDoe2025): Add abstract and doi.

 * biblio.bib (PriAllLop2024ppsn): Add doi and pages.
 * crossref.bib (PPSN2024): Fix editors and title. Add volume.

articles.bib

@@ -11953,9 +11953,27 @@ @Article{LopVerDreDoe2025
                   Single-objective Black-box Optimization Algorithms},
   journal      = tec,
   year         = 2025,
-  note         = {Accepted, pre-print available at
-                  \url{https://doi.org/10.48550/arXiv.2404.02031}},
-  doi          = {10.1109/TEVC.2024.3462758}
+  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.}
 }
 
 @Article{LouBoi2008vns_anytime,

biblio.bib

@@ -10552,6 +10552,7 @@ @InCollection{PriAllLop2024ppsn
                   Clyde} #and# {Benatan, Matt} #and# Knowles,
   title        = {An Adaptive Approach to Bayesian Optimization with Setup
                   Switching Costs},
+  pages        = {340--355},
   abstract     = {Black-box optimization methods typically assume that
                   evaluations of the black-box objective function are equally
                   costly to evaluate. We investigate here a
@@ -10563,9 +10564,9 @@ @InCollection{PriAllLop2024ppsn
                   accepting this additional cost to explore more of the search
                   space. We adapt two process-constrained batch algorithms to
                   this sequential problem formulation, and propose two new
-                  methods: one one cost-aware and one cost-ignorant. We validate
-                  and compare the algorithms using a set of 7 scalable test
-                  functions with different switching-cost settings. Our
+                  methods: one one cost-aware and one cost-ignorant. We
+                  validate and compare the algorithms using a set of 7 scalable
+                  test functions with different switching-cost settings. Our
                   proposed cost-aware parameter-free algorithm yields
                   comparable results to tuned process-constrained algorithms in
                   all settings we considered, suggesting some degree of
@@ -10578,7 +10579,8 @@ @InCollection{PriAllLop2024ppsn
                   the general class of resource-constrained problems, they are
                   particularly relevant to problems where adaptability to
                   varying resource availability is of high importance.},
-  crossref     = {PPSN2024}
+  crossref     = {PPSN2024},
+  doi          = {10.1007/978-3-031-70068-2_21}
 }
 
 @Book{PriStoLam2005:book,

crossref.bib

@@ -4256,29 +4256,32 @@ @Book{PPSN2020
 }
 
 @Book{PPSN2022,
-  booktitle =    ppsn17,
-  title =        {Parallel Problem Solving from Nature - PPSN XVII, 17th
+  editor       = Rudolph_G #and# { Anna V. Kononova } #and# Aguirre #and#
+                  Kerschke_P #and# Ochoa #and# Tusar,
+  title        = {Parallel Problem Solving from Nature - PPSN XVII, 17th
                   International Conference, PPSN 2022, Dortmund, Germany,
                   September 10-14, 2022, Proceedings, Part I},
-  editor =       Rudolph_G #and# " Anna V. Kononova " #and# Aguirre #and#
-                  Kerschke_P #and# Ochoa #and# Tusar,
-  series =       lncs,
-  publisher =    springer,
-  address =      add-cham,
-  year =         2022,
-  volume =       13398,
+  year         = 2022,
+  publisher    = springer,
+  booktitle    = ppsn17,
+  volume       = 13398,
+  series       = lncs,
+  address      = add-cham
 }
 
 @Book{PPSN2024,
-  booktitle =    ppsn18,
-  title =        {Parallel Problem Solving from Nature - PPSN XVIII, 18th
-                  International Conference, PPSN 2024},
-  editor =       Rudolph_G #and# " Anna V. Kononova " #and# Aguirre #and#
-                  Kerschke_P #and# Ochoa #and# Tusar,
-  series =       lncs,
-  publisher =    springer,
-  address =      add-cham,
-  year =         2024,
+  editor       = {Michael Affenzeller and Stephan M. Winkler and Anna
+                  V. Kononova} #and# Trautmann #and# Tusar #and# Machado_P
+                  #and# Baeck_Thomas,
+  title        = {Parallel Problem Solving from Nature - PPSN XVIII, 18th
+                  International Conference, PPSN 2024, Hagenberg, Austria,
+                  September 14-18, 2024, Proceedings, Part II},
+  year         = 2024,
+  publisher    = springer,
+  booktitle    = ppsn18,
+  volume       = 15149,
+  series       = lncs,
+  address      = add-cham
 }
 
 @Proceedings{PROC2013,