Special Issue on Benchmarking of Computational Intelligence Algorithms in the Applied Soft Computing Journal Completed

Today, our  Special Issue on Benchmarking of Computational Intelligence Algorithms in the Applied Soft Computing Journal (ASOC) has finally been completed. We accepted 14 articles, which either introduce new new benchmarks or benchmark generators, propose new visualization and evaluation methods, provide new tools, or study general topics in the field. Each article makes an important contribution to the art of analyzing and understanding the performance of computational intelligence methods. We are very thankful to the ASOC journal to allow us to make this happen – and especially for the outstanding support and the help provided by Mario Koeppen and Bas van Vlijmen of the editor team as well as Xinrui Wang, the publishing specialized at Elsevier. Of course, we are as same as thankful to our authors and reviewers, who put in a lot of work to ensure that all articles were refined and refined and refined again until they were perfect. Now that the editorial of the issue is out, this journey is complete. Benchmarking of optimization is an interesting topic which is gaining more and more momentum in the research community. For instance, there currently is a large community effort ongoing with the goal to gather good practices for benchmarking, which resulted in the technical report Benchmarking in Optimization: Best Practice and Open Issues and to which we are also contributing (and which directly picks up some of the topics tackled in our special issue).

1. Thomas Weise, Markus Wagner, Bin Li, Xingyi Zhang, and Jörg Lässig. Special Issue on Benchmarking of Computational Intelligence Algorithms in the Applied Soft Computing Journal (Editorial). Applied Soft Computing 93:106502. August 2020. doi:10.1016/j.asoc.2020.106502
2. Andreas Fischbach and Thomas Bartz-Beielstein. Improving the Reliability of Test Functions Generators. Applied Soft Computing 92:106315, July 2020. doi:10.1016/j.asoc.2020.106315.
3. Lucas Augusto Müller de Souza, José Eduardo Henriques da Silva, Luciano Jerez Chaves, and Heder Soares Bernardino. A Benchmark Suite for Designing Combinational Logic Circuits via Metaheuristics. Applied Soft Computing 91:106246, June 2020, doi:10.1016/j.asoc.2020.106246.
4. Urban Škvorc, Tome Eftimov, and Peter Korošec. Understanding the Problem Space in Single-Objective Numerical Optimization using Exploratory Landscape Analysis. Applied Soft Computing 90:106138, May 2020, doi:10.1016/j.asoc.2020.106138.
5. Ivan Reinaldo Meneghini, Marcos Antonio Alves, António Gaspar-Cunha, and Frederico Gadelha Guimarães. Scalable and Customizable Benchmark Problems for Many-Objective Optimization. Applied Soft Computing 90:106139, May 2020, doi:10.1016/j.asoc.2020.106139.
6. Zhi-Ze Wu, Shou-Hong Wan, Xiao-Feng Wang, Ming Tan, Le Zou, Xin-Lu Li, and Yan Chen. A Benchmark Data Set for Aircraft Type Recognition from Remote Sensing Images. Applied Soft Computing 89:106132, April 2020, doi:10.1016/j.asoc.2020.106132.
7. Mihaela Oprea. A General Framework and Guidelines for Benchmarking Computational Intelligence Algorithms Applied to Forecasting Problems Derived from an Application Domain-Oriented Survey. Applied Soft Computing 89:106103, April 2020, doi:10.1016/j.asoc.2020.106103.
8. Ryoji Tanabe and Hisao Ishibuchi. An Easy-to-Use Real-World Multi-Objective Optimization Problem Suite. Applied Soft Computing 89:106078, April 2020, doi:10.1016/j.asoc.2020.106078.
9. Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, Thomas Bäck. Benchmarking Discrete Optimization Heuristics with IOHprofiler. Applied Soft Computing 88:106027, March 2020, doi:10.1016/j.asoc.2019.106027.
10. David J. Walker and Matthew J. Craven. Identifying Good Algorithm Parameters in Evolutionary Multi- and Many-Objective Optimisation: A Visualisation Approach. Applied Soft Computing 88:105902, March 2020, doi:10.1016/j.asoc.2019.105902.
11. Jakob Bossek, Pascal Kerschke, and Heike Trautmann. A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms Applied Soft Computing 88:105901, March 2020, doi:10.1016/j.asoc.2019.105901.
12. Tome Eftimov, Gašper Petelin, and Peter Korošec. DSCTool: A Web-Service-based Framework for Statistical Comparison of Stochastic Optimization Algorithms. Applied Soft Computing 87:105977, February 2020, doi:10.1016/j.asoc.2019.105977.
13. Andrea Tangherloni, Simone Spolaor, Paolo Cazzaniga, Daniela Besozzi, Leonardo Rundo, Giancarlo Mauri, Marco S. Nobile Biochemical Parameter Estimation vs. Benchmark Functions: A Comparative Study of Optimization Performance and Representation Design. Applied Soft Computing 81:105494, August 2019, doi:10.1016/j.asoc.2019.105494.
14. Muwei Jian, Qiang Qi, Hui Yu, Junyu Dong, Chaoran Cui, Xiushan Nie, Huaxiang Zhang, Yilong Yin, Kin-Man Lam. The Extended Marine Underwater Environment Database and Baseline Evaluations. Applied Soft Computing 80:425-437, July 2019, doi:10.1016/j.asoc.2019.04.025.
15. Sina Torabi and Mattias Wahde. A Method for Performance Analysis of a Genetic Algorithm Applied to the Problem of Fuel Consumption Minimization for Heavy-Duty Vehicles. Applied Soft Computing 80:735-741, July 2019, doi:10.1016/j.asoc.2019.04.042.