added Applied Sciences editorial and arxiv

.sitesrc/cicirello-pubs-web.bib

@@ -1,56 +1,34 @@
-@SPECIALISSUE{APP-SCI-SpecialIssue2023,
-EDITOR = {Vincent A. Cicirello},
-TITLE = {Special Issue on Evolutionary Computation: Theories, Techniques, and Applications},
+@ARTICLE{APP-SCI-SpecialIssue2023,
+AUTHOR = {Vincent A. Cicirello},
+TITLE = {Evolutionary Computation: Theories, Techniques, and Applications},
 JOURNAL = {Applied Sciences},
+VOLUME = {14},
+NUMBER = {6},
+ARTICLENO = {2542},
+MONTH = {March},
 YEAR = {2024},
-MONTH = {January},
+NUMPAGES = {6},
+DOI = {https://doi.org/10.3390/app14062542},
 ACRO = {applsci},
-DOI = {https://www.mdpi.com/journal/applsci/special_issues/Evolutionary_Computation},
-graphicalAbstract = {GraphicalAbstract-SI-on-Evolutionary-Computation.png},
-ABSTRACT = {Evolutionary computation offers powerful problem-solving methodologies 
-inspired by models of natural genetics and evolutionary processes. Potential 
-applications are wide-ranging and include problems from combinatorial optimization, 
-numerical optimization, multi-objective optimization, and others as well as specific 
-applications of these problems in diverse domains, such as engineering, design, 
-medicine, robotics, science, etc. Techniques from evolutionary computation often 
-lend themselves well to parallel and distributed implementations and are often more 
-effective in dealing with challenging problem characteristics such as non-linearity 
-and high-dimensionality than alternative approaches.
-</p><p>
-This Special Issue invites submissions on recent advances in the theory and 
-applications of evolutionary computation. Papers on all forms of evolutionary computation 
-are welcome, including but not limited to genetic algorithms, genetic programming, 
-evolution strategies, evolutionary programming, and memetic algorithms, as well as 
-papers at the intersection of evolutionary computation and machine learning, such 
-as neuroevolution. Additionally, we welcome papers that employ closely related 
-techniques such as simulated annealing, ant colony optimization, particle swarm optimization, 
-artificial immune systems, and other metaheuristics. All application areas are welcome, 
-but submissions that deal with problems in the applied sciences are especially encouraged.
-</p><p>
-The special issue covers the following non-exclusive list of topics:</p>
-<ul>
-<li>ant colony optimization;</li>
-<li>artificial immune systems;</li>
-<li>combinatorial optimization;</li>
-<li>evolution strategies;</li>
-<li>evolutionary computation;</li>
-<li>evolutionary data mining;</li>
-<li>evolutionary machine learning;</li>
-<li>evolutionary programming;</li>
-<li>evolvable hardware;</li>
-<li>genetic algorithms;</li>
-<li>genetic programming;</li>
-<li>memetic algorithms;</li>
-<li>metaheuristics;</li>
-<li>multi-objective optimization;</li>
-<li>neuroevolution;</li>
-<li>numerical optimization;</li>
-<li>particle swarm optimization;</li>
-<li>simulated annealing.</li>
-</ul>
-<p>Submissions opened May 2022 with a deadline of January 30, 2024.}
+PDF = {applsci-14-02542.pdf},
+graphicalAbstract = {GraphicalAbstract-SI-on-Evolutionary-Computation.png}
+}
+
+@techreport{ALG-24-007,
+  title = {On the Average Runtime of an Open Source Binomial Random Variate Generation Algorithm},
+  author = {Vincent A. Cicirello},
+  year = {2024},
+  month = {March},
+  number = {ALG-24-007},
+  type = {Technical Report},
+  institution = {Cicirello.org},
+  pdf = {https://reports.cicirello.org/24/007/ALG-24-007.pdf},
+  infopage = {https://reports.cicirello.org/24/007/},
+  code = {https://github.com/cicirello/btpe-iterations},
+  acro = {techreport},
+  arxiv = {arXiv:2403.11018},
+  doi = {https://arxiv.org/abs/2403.11018}
 }
-% THIS IS A NONSTANDARD TYPE... WILL NOT WORK WITH BIBTEX
 
 @INPROCEEDINGS{ECTA2023,
 author = {Vincent A. Cicirello},