Our institute welcomes Dr. Markus Wagner, Senior Lecturer from the Optimisation and Logistics Group of the School of Computer Science of The University of Adelaide, SA, Australia, for a research visit from January 2 to January 7. The members of the Optimisation and Logistics Group in Adelaide research optimization methods that are frequently used to solve hard and complex optimization problems. These include linear programming, branch and bound, genetic algorithms, evolution strategies, genetic programming, ant colony optimization, local search, and others. The areas of interest of Dr. Wagner are heuristic optimization and applications thereof. His work draws on computational complexity analysis and on performance landscape analysis. This is his second visit to our group, after his stay here last October/November. Dr. Wagner and the members of our institute will spend most of his visiting time to continue the work we started back then, i.e., on joint research and on developing future joint projects and collaborations.
Dr. Markus Wagner is a Senior Lecturer at the School of Computer Science, University of Adelaide, Australia. He has done his PhD studies at the Max Planck Institute for Informatics in Saarbrücken, Germany and at the University of Adelaide, Australia. His research topics range from mathematical runtime analysis of heuristic optimization algorithms and theory-guided algorithm design to applications of heuristic methods to renewable energy production, professional team cycling and software engineering. So far, he has been a program committee member 30 times, and he has written over 70 articles with over 70 different co-authors. He has chaired several education-related committees within the IEEE CIS, is Co-Chair of ACALCI 2017 and General Chair of ACALCI 2018. Dr. Wagner is also a co-chair of our workshops Black-Box Discrete Optimization Benchmarking (BB-DOB@GECCO) Workshop, Black-Box Discrete Optimization Benchmarking (BB-DOB@PPSN), both organized together with Profs. Pietro S. Oliveto (University of Sheffield), Thomas Weise, Borys Wróbel (Adam Mickiewicz University), and Aleš Zamuda (University of Maribor), as well as the Workshop International Workshop on Benchmarking of Computational Intelligence Algorithms (BOCIA) and a co-guest editor of the Special Issue on Benchmarking of Computational Intelligence Algorithms in the Computational Intelligence Journal with Profs. Thomas Weise, Bin Li (USTC), Xingyi Zhang (Anhui University), and Jörg Lässig (University of Applied Sciences Zittau/Görlitz).
Today, Prof. Dr. Thomas Weise has joined the Editorial Board of the Applied Soft Computing journal published by Elsevier and indexed by SCI and EI with an impact factor of 3.541 during the last two years and 3.811 during the previous five years. The topics of this journal and the research focus of our Institute fit very closely together: we develop tailor-made applications of optimization, Operations Research, Computational Intelligence, Evolutionary Computation, Metaheuristics, as well as Machine Learning and Data Mining for industry partners to help them to become more efficient, faster, and environmentally friendlier while, at the same time, reducing costs and improving product and service quality. Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
The Black-Box Discrete Optimization Benchmarking Workshop (BB-DOB@PPSN) has just been accepted to be a part of the Fifteenth International Conference on Parallel Problem Solving from Nature (PPSN XV), September 8-12, 2018, in Coimbra, Portugal: http://ppsn2018.dei.uc.pt/).
The Black-Box-Optimization Benchmarking (BBOB) methodology introduced by the long-standing and successful BBOB-GECCO workshops series has become a well-established standard for benchmarking continuous optimization algorithms. The aim of workshop is to develop a similar standard methodology for the benchmarking of black-box optimization algorithms for discrete and combinatorial domains. The goal of this second edition of the BB-DOB workshop series is to define a suitable set of benchmark functions for discrete and combinatorial optimization problems.
Our workshop is cordially inviting the submission of original and unpublished research papers. Here you can download the BB-DOB@PPSN Workshop Call for Papers (CfP) in PDF format and here as plain text file. The deadline for workshop submissions will be June 26, 2018.
The Black-Box Discrete Optimization Benchmarking Workshop (BB-DOB@GECCO) has just been accepted to be a part of the Genetic and Evolutionary Computation Conference (GECCO 2018), taking place on July 15-19, 2018, in Kyoto, Japan (http://gecco-2018.sigevo.org/).
Quantifying and comparing the performance of optimization algorithms is one important aspect of research in search and optimization. The Black-Box-Optimization Benchmarking (BBOB) methodology introduced by the long-standing and successful BBOB-GECCO workshops series has become a well-established standard for benchmarking continuous optimization algorithms. The aim of this workshop is to develop a similar standard methodology for the benchmarking of black-box optimization algorithms for discrete and combinatorial domains. The goal of this first edition of the BB-DOB workshop series is to define a suitable set of benchmark functions for discrete and combinatorial optimization problems.
Our workshop is cordially inviting the submission of original and unpublished research papers. Here you can download the BB-DOB@GECCO Workshop Call for Papers (CfP) in PDF format and here as plain text file. The deadline for workshop submissions will be April 3, 2018.
The University Curricula Committee of the IEEE Computational Intelligence Society (IEEE CIS) has created a new subreddit, named /r/CompIntellCourses, in order to build a collection of links to good quality courses on "everything CI." Teachers all over the world are invited to submit their courses, if the teaching material available for free. This is a very nice way to produce an organized catalog of high-quality lectures in the field, making it easier for the everybody to learn about some CI technologies from scratch, or to keep up-to-date with new trends.
Our course Metaheuristic Optimization is one of the first courses that has been included in this list. Metaheuristic optimization is concerned with solving computationally hard problems using soft computing methods and therefore is a field of Computational Intelligence. Our course is based on our previous courses Practical Optimization Algorithm Design taught by Prof. Weise at University of Science and Technology of China (USTC) [中国科学技术大学] and some of the units in the course Evolutionary Computation – Theory and Application which also were taught by Prof. Weise at USTC until 2016. It is, however, significantly extended, improved, and adapted to be an introduction to and overview of the field for both graduate students and research team members in our group at Hefei University [合肥学院].
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