Dr. Thomas Weise

Research Interests

My major research interests include

Benchmarking of Optimization Methods

Optimization is a technology which can become even more important than data mining and big data are now. Optimization means to find approximate solutions for hard problems. Optimization algorithms can find short routes in logistic planning scenarios, construction plans for work pieces that require only little amounts of material, or efficient schedules in production planning. They can help to reduce costs and pollution at the same time.

"Optimization algorithms" implies a plural, and I want to know how we can find out which method is best for which problem and when and why. First, using the best algorithm will give us the best solutions, which is what we want. Knowing why an algorithm is best could help us to create even better ones. Thus, benchmarking of optimization algorithms in a statistically sound, robust, and ideally automatic way seems to be an important topic to me. Yet I find it sort of under-represented in literature.

Recently, together with some colleagues, I published the TSP Suite, a Java experimentation framework for the Traveling Salesman Problem (TSP). This framework follows a holistic approach: It supports researchers in implementing and JUnit testing their algorithms, running experiments in a parallel and distributed fashion, and has an evaluator component which can compare the performance of different algorithms over time according to several different statistical measures automatically.

With my work on this topic, I want to make it easier for other researchers (and me myself, obviously) to run comprehensive experiments and to statistically rigorously evaluate algorithms. I believe that if we want to do good research in the domain of EC, we need sound experiments and clear and comprehensive algorithm comparisons. This, however, costs a lot of work and thus, tool support is needed. I think our holistic in TSP Suite approach is the right idea for this.

My research in this field has resulted in the following publications:

  1. Yuezhong Wu, Thomas Weise, and Weichen Liu. Hybridizing Different Local Search Algorithms with Each Other and Evolutionary Computation: Better Performance on the Traveling Salesman Problem. In Proceedings of the 18th Genetic and Evolutionary Computation Conference (GECCO'16), Denver, Colorado, USA, July 20–24, 2016, New York, NY, USA: Association for Computing Machinery (ACM). accepted for publication
    details
  2. Thomas Weise and Raymond Chiong. An Alternative Way of Presenting Statistical Test Results when Evaluating the Performance of Stochastic Approaches. Neurocomputing, 147:235-238, January 5, 2015.
    Supersedes technical report Thomas Weise: “Illustration of Statistical Test Results for Experiment Evaluation”, 2011
    details / doi:10.1016/j.neucom.2014.06.071 / pdf icon pdf
  3. Thomas Weise, Raymond Chiong, Ke Tang, Jörg Lässig, Shigeyoshi Tsutsui, Wenxiang Chen, Zbigniew Michalewicz, and Xin Yao. Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem. IEEE Computational Intelligence Magazine (CIM), 9(3):40-52, August 2014. Featured article and selected paper at the website of the IEEE Computational Intelligence Society (http://cis.ieee.org/).
    details / doi:10.1109/MCI.2014.2326101 / pdf icon pdf
  4. Thomas Weise. TSP, Benchmarking, and EC. In Zhi-Hua and Yang Yu, editors, The 8th International Workshop on Nature Inspired Computation and Applications (NICaiA'13 Autumn), Nanjing, Jiangsu, China: Nanjing University, Xianlin Campus, Department of Computer Science and Technology, National Key Laboratory for Novel Software Technology, Learning And Mining from DatA group (LAMBDA), October 17–19, 2013.
  5. Ke Tang, Zhenyu Yang, and Thomas Weise. Special Session on Evolutionary Computation for Large Scale Global Optimization at 2012 IEEE World Congress on Computational Intelligence (CEC@WCCI-2012). Technical Report, Hefei, Anhui, China: University of Science and Technology of China (USTC), School of Computer Science and Technology, Nature Inspired Computation and Applications Laboratory (NICAL), June 14, 2012.
  6. Thomas Weise. Illustration of Statistical Test Results for Experiment Evaluation. Technical Report, Hefei, Anhui, China: University of Science and Technology of China (USTC), School of Computer Science and Technology, Nature Inspired Computation and Applications Laboratory (NICAL), March 2, 2011.
  7. Thomas Weise, Li Niu, and Ke Tang. AOAB – Automated Optimization Algorithm Benchmarking. In Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO'10), pages 1479-1486, Portland, OR, USA: Portland Marriott Downtown Waterfront Hotel, July 7-11, 2010. ISBN: 978-1-4503-0073-5, New York, NY, USA: ACM Press.
    details / doi:10.1145/1830761.1830763 / pdf icon pdf / pdf icon slides
  8. Ke Tang, Xiaodong Li, Ponnuthurai Nagaratnam Suganthan, Zhenyu Yang, and Thomas Weise. Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale Global Optimization. Technical Report, Hefei, Anhui, China: University of Science and Technology of China (USTC), School of Computer Science and Technology, Nature Inspired Computation and Applications Laboratory (NICAL), January 8, 2010.
  9. Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs. A Tunable Model for Multi-Objective, Epistatic, Rugged, and Neutral Fitness Landscapes. In Maarten Keijzer, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H. Holmes, Gregory S. Hornby, Daniel Howard, James Kennedy, Sanjeev P. Kumar, Fernando G. Lobo, Julian Francis Miller, Jason H. Moore, Frank Neumann, Martin Pelikan, Jordan B. Pollack, Kumara Sastry, Kenneth Owen Stanley, Adrian Stoica, El-Ghazali, and Ingo Wegener, editors, Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO'08), pagran 795-802, July 12-16, 2008, Renaissance Atlanta Hotel Downtown: Atlanta, GA, USA. ISBN: 978-1-60558-130-9, New York, NY, USA: ACM Press.
    details / doi:10.1145/1389095.1389252 / pdf icon pdf / pdf icon slides / java icon code

Logistic Planning

Logistics and transport are one of the most important services for any industry or society. Without them, the economy would simply break down or fall back to pre-industrial levels. However, they also turn oil (which is getting less) into pollution. Using optimization algorithms for logistic planning means to find ways to transport goods or people in an efficient way. Depending on the problem, efficient could mean to travel short distances, use few vehicles and less manpower, or to be otherwise cheap. This often equates to being environmentally friendlier. Research on this domain therefore is, in my opinion, highly relevant and I want to contribute to it.

A real contribution constitutes should be rigorously evaluated and shown to provide improvements in a robust way, under many scenarios, which leads me back to my first research interest, proper benchmarking. Anyway, I have been working in this domain since 2008, both on theoretical/abstract as well as practical/real-world problems:

  • Traveling Salesman Problem (TSP)
  • Capacitated Arc Routing Problem (CARP)
  • Vehicle Routing Problem with Time Windows (VRPTW)
  • Real-World Problem in the in.west project, involving different vehicles, trains, time windows, different capacity limits, etc. in the domain of swap-body based transportation in a nation-scale by a commercial logistics company

My research in this field has resulted in the following publications:

  1. Yuezhong Wu, Thomas Weise, and Weichen Liu. Hybridizing Different Local Search Algorithms with Each Other and Evolutionary Computation: Better Performance on the Traveling Salesman Problem. In Proceedings of the 18th Genetic and Evolutionary Computation Conference (GECCO'16), Denver, Colorado, USA, July 20–24, 2016, New York, NY, USA: Association for Computing Machinery (ACM). accepted for publication
    details
  2. Weichen Liu, Thomas Weise, Yuezhong Wu, Dan Xu, and Raymond Chiong. Hybrid Ejection Chain Methods for the Traveling Salesman Problem. Journal of Computational and Theoretical Nanoscience accepted for publication on January 20, 2016.
    details
  3. Wei Shi, Thomas Weise, Raymond Chiong, and Bülent Çatay. Hybrid PACO with Enhanced Pheromone Initialization for Solving the Vehicle Routing Problem with Time Windows. In Proceedings of the 2015 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS'15) Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI'15), ISBN: 978-1-4799-7560-0, pages 1735–1742, Cape Town, South Africa: Cape Town International Convention Center, December 8–10, 2015. Los Alamitos, CA, USA: IEEE Computer Society Press.
    details / doi:10.1109/SSCI.2015.242 / pdf icon pdf / pdf icon slides
  4. Weichen Liu, Thomas Weise, Yuezhong Wu, and Raymond Chiong. Hybrid Ejection Chain Methods for the Traveling Salesman Problem. In Proceedings of the 10th International Conference on Bio-Inspired Computing – Theories and Applications (BIC-TA'15), Maoguo Gong, Linqiang Pan, Tao Song, Ke Tang, and Xingyi Zhang, editors, September 25–28, 2015, Hefei, Anhui, China, volume 562 of Communications in Computer and Information Science. Berlin/Heidelberg: Springer-Verlag, pages 268–282, ISBN 978-3-662-49013-6.
    details / doi:10.1007/978-3-662-49014-3_25 / pdf icon pdf
  5. Dan Xu, Thomas Weise, Yuezhong Wu, Jörg Lässig, and Raymond Chiong. An Investigation of Hybrid Tabu Search for the Traveling Salesman Problem. In Proceedings of the 10th International Conference on Bio-Inspired Computing – Theories and Applications (BIC-TA'15), Maoguo Gong, Linqiang Pan, Tao Song, Ke Tang, and Xingyi Zhang, editors, September 25–28, 2015, Hefei, Anhui, China, volume 562 of Communications in Computer and Information Science. Berlin/Heidelberg: Springer-Verlag, pages 523–537, ISBN 978-3-662-49013-6.
    details / doi:10.1007/978-3-662-49014-3_47 / pdf icon pdf
  6. Yuezhong Wu, Thomas Weise, and Raymond Chiong. Local Search for the Traveling Salesman Problem: A Comparative Study. In Proceedings of the 14th IEEE Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC'15), July 6–8, 2015, Beijing, China, pages 213–220, ISBN: 978-1-4673-7289-3.
    details / doi:10.1109/ICCI-CC.2015.7259388 / pdf icon pdf
  7. Yan Jiang, Thomas Weise, Jörg Lässig, Raymond Chiong, and Rukshan Athauda. Comparing a Hybrid Branch and Bound Algorithm with Evolutionary Computation Methods, Local Search and their Hybrids on the TSP. In Proceedings of the IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS'14), Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI'14), Orlando, FL, USA: Caribe Royale All-Suite Hotel and Convention Center, December 9–12, 2014, pages 148–155. Los Alamitos, CA, USA: IEEE Computer Society Press. ISBN 978-1-4799-5375-2.
    details / doi:10.1109/CIPLS.2014.7007174 / pdf icon pdf / pdf icon poster
  8. Thomas Weise, Raymond Chiong, Ke Tang, Jörg Lässig, Shigeyoshi Tsutsui, Wenxiang Chen, Zbigniew Michalewicz, and Xin Yao. Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem. IEEE Computational Intelligence Magazine (CIM), 9(3):40-52, August 2014. Featured article and selected paper at the website of the IEEE Computational Intelligence Society (http://cis.ieee.org/).
    details / doi:10.1109/MCI.2014.2326101 / pdf icon pdf
  9. Wei Shi and Thomas Weise. An Initialized ACO for the VRPTW. In Hujun Yin, Ke Tang, Yang Gao, Frank Klawonn, Minho Lee, Thomas Weise, Bin Li, and Xin Yao, editors, Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'13), volume 8206/2013 of Lecture Notes in Computer Science (LNCS), pages 93-100, Hefei, Anhui, China: Empark Grand Hotel, October 20–23, 2013. ISBN: 978-3-642-41277-6, Berlin, Germany: Springer-Verlag GmbH.
    details / doi:10.1007/978-3-642-41278-3_12 / pdf icon pdf
  10. Thomas Weise. TSP, Benchmarking, and EC. In Zhi-Hua and Yang Yu, editors, The 8th International Workshop on Nature Inspired Computation and Applications (NICaiA'13 Autumn), Nanjing, Jiangsu, China: Nanjing University, Xianlin Campus, Department of Computer Science and Technology, National Key Laboratory for Novel Software Technology, Learning And Mining from DatA group (LAMBDA), October 17–19, 2013.
  11. Jin Ouyang, Thomas Weise, Alexandre Devert, and Raymond Chiong. SDGP: A Developmental Approach for Traveling Salesman Problems. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS'13), Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI'13), pages 78-85, Singapore: Grand Copthorne Waterfront Hotel, April 15–19, 2013. ISBN: 9781467359054, Los Alamitos, CA, USA: IEEE Computer Society Press.
    details / doi:10.1109/CIPLS.2013.6595203 / pdf icon pdf / pdf icon slides
  12. Thomas Weise, Alexandre Devert, and Ke Tang. A Developmental Solution to (Dynamic) Capacitated Arc Routing Problems using Genetic Programming. In Terence Soule and Jason H. Moore, editors, Proceedings of the 14th Genetic and Evolutionary Computation Conference (GECCO'12), pages 831-838, Philadelphia, PA, USA: Doubletree by Hilton Hotel Philadelphia Center City, July 7–11, 2012. ISBN: 978-1-4503-1177-9, New York, NY, USA: Association for Computing Machinery (ACM).
    details / doi:10.1145/2330163.2330278 / pdf icon pdf / pdf icon slides
  13. Thomas Weise. Representations for Logistic Planning. In Xin Yao, editor, The Third NICaiA Workshop on Nature Inspired Computation and Its Applications (NICaiA'12), Birmingham, UK: University of Birmingham, Computer Science Building, April 16–17, 2012.
  14. Thomas Weise, Alexander Podlich, Manfred Menze, and Christian Gorldt. Optimierte Güterverkehrsplanung mit Evolutionären Algorithmen. Industrie Management – Zeitschrift für industrielle Geschäftsprozesse, 10(3):37-40, June 2, 2009.
    details / pdf icon pdf
  15. Thomas Weise, Alexander Podlich, Kai Reinhard, Christian Gorldt, and Kurt Geihs. Evolutionary Freight Transportation Planning. In Mario Giacobini, Penousal Machado, Anthony Brabazon, Jon McCormack, Stefano Cagnoni, Michael O'Neill, Gianni A. Di Caro, Ferrante Neri, Anikó Ekárt, Mike Preuß, Anna Isabel Esparcia-Alcázar, Franz Rothlauf, Muddassar Farooq, Ernesto Tarantino, Andreas Fink, and Shengxiang Yang, editors, Applications of Evolutionary Computing – Proceedings of EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG (EvoWorkshops'09), volume 5484/2009 of Lecture Notes in Computer Science (LNCS), pages 768-777, Tübingen, Germany: Eberhard-Karls-Universität Tübingen, Fakultät für Informations- und Kognitionswissenschaften, April 15–17, 2009. ISBN: 978-3-642-01128-3, Berlin, Germany: Springer-Verlag GmbH.
    details / doi:10.1007/978-3-642-01129-0_87 / pdf icon pdf / pdf icon slides
  16. Alexander Podlich, Thomas Weise, Manfred Menze, and Christian Gorldt. Intelligente Wechselbrückensteuerung für die Logistik von Morgen. Electronic Communications of the EASST, 17(205):1-11, Special Issue: Michael Wagner, Dieter Hogrefe, Kurt Geihs, and Klaus David, editors, “Kommunikation in Verteilten Systemen 2009”, KiVS'09, Proceedings, Workshops der Wissenschaftlichen Konferenz Kommunikation in Verteilten Systemen 2009 (WowKiVS 2009), Kassel, Hesse, Germany: University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, March 6, 2009. Potsdam, Germany: European Association of Software Science and Technology (EASST; Universität Potsdam, Institute for Informatics).
    details / doi:10.14279/tuj.eceasst.17.205 / pdf icon pdf / link
  17. Thomas Weise, Alexander Podlich, and Christian Gorldt. Solving Real-World Vehicle Routing Problems with Evolutionary Algorithms. In Raymond Chiong and Sandeep Dhakal, editors, Natural Intelligence for Scheduling, Planning and Packing Problems, volume 250 of Studies in Computational Intelligence, chapter 2, pages 29-53. ISBN: 978-3-642-04038-2, Berlin/Heidelberg: Springer-Verlag, October 2009.
    details / doi:10.1007/978-3-642-04039-9_2 / pdf icon pdf

Genetic Programming

During my PhD studies starting in 2005, I was working on Genetic Programming (GP). GP is normally used to synthesize (approximate) mathematical formulas over continuous domains or to evolve Boolean functional expressions. Although I also researched such applications of GP, the focus of my work was to evolve exact integer algorithms, either for local or distributed computations.

This domain has a lot of problematic features which are hard to come by. I think I made a little progress here and there, but the question whether this can ever be done efficiently enough for any practical application is still unanswered (which means that, for the time being, the answer is no, but maybe we can fix that). Still, from dealing with next-to-impossible applications of an optimization method, I think I learned a lot. I am currently not doing any active work in this domain, but I still care about it, i.e., would be happy to supervise students interested in it and/or to co-operate with researchers who are.

My research in this field has resulted in the following publications:

  1. Thomas Weise, Raymond Chiong, Ke Tang, Jörg Lässig, Shigeyoshi Tsutsui, Wenxiang Chen, Zbigniew Michalewicz, and Xin Yao. Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem. IEEE Computational Intelligence Magazine (CIM), 9(3):40-52, August 2014. Featured article and selected paper at the website of the IEEE Computational Intelligence Society (http://cis.ieee.org/).
    details / doi:10.1109/MCI.2014.2326101 / pdf icon pdf
    (includes experiment with GP)
  2. Thomas Weise, Mingxu Wan, Ke Tang, and Xin Yao. Evolving Exact Integer Algorithms with Genetic Programming. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC'14), Proceedings of the 2014 World Congress on Computational Intelligence (WCCI'14), pages 1816-1823, Beijing, China: Beijing International Convention Center (BICC), July 6–11, 2014. ISBN: 978-1-4799-1488-3, Los Alamitos, CA, USA: IEEE Computer Society Press.
    details / doi:10.1109/CEC.2014.6900292 / pdf icon pdf / pdf icon slides
  3. Thomas Weise, Mingxu Wan, Ke Tang, Pu Wang, Alexandre Devert, and Xin Yao. Frequency Fitness Assignment. IEEE Transactions on Evolutionary Computation (IEEE-EC), 18(2):226-243, April 2014.
    details / doi:10.1109/TEVC.2013.2251885 / pdf icon pdf
    (includes two experiments with GP)
  4. Pu Wang, Ke Tang, Thomas Weise, Edward P.K. Tsang, and Xin Yao. Multiobjective Genetic Programming for Maximizing ROC Performance. Neurocomputing, 125:102-118, February 11, 2014.
    details / doi:10.1016/j.neucom.2012.06.054 / pdf icon pdf
  5. Thomas Weise. TSP, Benchmarking, and EC. In Zhi-Hua and Yang Yu, editors, The 8th International Workshop on Nature Inspired Computation and Applications (NICaiA'13 Autumn), Nanjing, Jiangsu, China: Nanjing University, Xianlin Campus, Department of Computer Science and Technology, National Key Laboratory for Novel Software Technology, Learning And Mining from DatA group (LAMBDA), October 17–19, 2013.
    (includes experiment with GP)
  6. Jin Ouyang, Thomas Weise, Alexandre Devert, and Raymond Chiong. SDGP: A Developmental Approach for Traveling Salesman Problems. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS'13), Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI'13), pages 78-85, Singapore: Grand Copthorne Waterfront Hotel, April 15–19, 2013. ISBN: 9781467359054, Los Alamitos, CA, USA: IEEE Computer Society Press.
    details / doi:10.1109/CIPLS.2013.6595203 / pdf icon pdf / pdf icon slides
  7. Thomas Weise, Alexandre Devert, and Ke Tang. A Developmental Solution to (Dynamic) Capacitated Arc Routing Problems using Genetic Programming. In Terence Soule and Jason H. Moore, editors, Proceedings of the 14th Genetic and Evolutionary Computation Conference (GECCO'12), pages 831-838, Philadelphia, PA, USA: Doubletree by Hilton Hotel Philadelphia Center City, July 7–11, 2012. ISBN: 978-1-4503-1177-9, New York, NY, USA: Association for Computing Machinery (ACM).
    details / doi:10.1145/2330163.2330278 / pdf icon pdf / pdf icon slides
  8. Thomas Weise and Ke Tang. Evolving Distributed Algorithms with Genetic Programming. IEEE Transactions on Evolutionary Computation (IEEE-EC), 16(2):242-265, April 2012. Received pdf icon CIS Publication Spotlight in the August 2012 issue of the IEEE Computational Intelligence Magazine (CIM).
    details / doi:10.1109/TEVC.2011.2112666 / pdf icon pdf
  9. Thomas Weise. Representations for Logistic Planning. In Xin Yao, editor, The Third NICaiA Workshop on Nature Inspired Computation and Its Applications (NICaiA'12), Birmingham, UK: University of Birmingham, Computer Science Building, April 16–17, 2012.
  10. Mingxu Wan, Thomas Weise, and Ke Tang. Novel Loop Structures and the Evolution of Mathematical Algorithms. In Sara Silva, James A. Foster, Miguel Nicolau, Penousal Machado, and Mario Giacobini, editors, Proceedings of the 14th European Conference on Genetic Programming (EuroGP'11), volume 6621/2011 of Lecture Notes in Computer Science (LNCS), pages 49-60, Torino, Italy, April 27–29, 2011. ISBN: 978-3-642-20406-7, Berlin, Germany: Springer-Verlag GmbH.
    details / doi:10.1007/978-3-642-20407-4_5 / pdf icon pdf / pdf icon slides
  11. Pu Wang, Thomas Weise, and Raymond Chiong. Novel Evolutionary Algorithms for Supervised Classification Problems: An Experimental Study. Evolutionary Intelligence, 4(1):3-16, March 2011.
    details / doi:10.1007/s12065-010-0047-7 / pdf icon pdf
  12. Pu Wang, Edward P.K. Tsang, Thomas Weise, Ke Tang, and Xin Yao. Using GP to Evolve Decision Rules for Classification in Financial Data Sets. In Fuchun Sun, Yingxu Wang, Jianhua Lu, Bo Zhang, Witold Kinsner, and Lotfi A. Zadeh, editors, Proceedings of the 9th IEEE International Conference on Cognitive Informatics (ICCI'10), pages 720-727, Beijing, China: Tsinghua University, July 7–9, 2010. ISBN: 978-1-4244-8040-1, Los Alamitos, CA, USA: IEEE Computer Society Press.
    details / doi:10.1109/COGINF.2010.5599820 / pdf icon pdf / pdf icon slides
  13. Thomas Weise and Raymond Chiong. Evolutionary Data Mining Approaches for Rule-based and Tree-based Classifiers. In Fuchun Sun, Yingxu Wang, Jianhua Lu, Bo Zhang, Witold Kinsner, and Lotfi A. Zadeh, editors, Proceedings of the 9th IEEE International Conference on Cognitive Informatics (ICCI'10), pages 696-703, Beijing, China: Tsinghua University, July 7–9, 2010. ISBN: 978-1-4244-8040-1, Los Alamitos, CA, USA: IEEE Computer Society Press.
    details / doi:10.1109/COGINF.2010.5599821 / pdf icon pdf / pdf icon slides
  14. Thomas Weise and Raymond Chiong. Evolutionary Approaches and Their Applications to Distributed Systems. In Raymond Chiong, editor, Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, chapter 6, pages 114-149. ISBN: 978-1-60566-798-0, Hershey, PA, USA: Information Science Reference / IGI Global, September 2009.
    details / doi:10.4018/978-1-60566-798-0.ch006
    (also discusses GP-based approaches)
  15. Thomas Weise and Michael Zapf. Evolving Distributed Algorithms with Genetic Programming: Election. In Lihong Xu, Erik D. Goodman, and Yongsheng Ding, editors, Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation (GEC'09), pages 577-584, Shanghai, China: Hua-Ting Hotel & Towers, June 12–14, 2009. ISBN: 978-1-60558-326-6, New York, NY, USA: ACM Press.
    details / doi:10.1145/1543834.1543913 / pdf icon pdf
  16. Thomas Weise. Evolving Distributed Algorithms with Genetic Programming. PhD thesis, Kassel, Hesse, Germany: University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, Distributed Systems Group, May 4, 2009.
    Won the pdf icon Dissertation Award of The Association of German Engineers (Verein Deutscher Ingenieure, VDI) Nordhessen.
    urn:nbn:de:hebis:34-2009051127217
    pdf icon dissertation / pdf icon defense slides / jar icon animations / pdf icon supplementary slides
  17. Thomas Weise, Michael Zapf, Mohammad Ullah Khan, and Kurt Geihs. Combining Genetic Programming and Model-Driven Development. International Journal of Computational Intelligence and Applications (IJCIA), 8(1):37-52, March 2009, Brijesh Verma, editor.
    details / doi:10.1142/S1469026809002436 / pdf icon pdf
  18. Thomas Weise. Global Optimization Algorithms – Theory and Application. Germany: it-weise.de (self-published), 2009.
    details / pdf icon pdf
    (contains large chapter on GP)
  19. Michael Zapf and Thomas Weise. Can Solutions Emerge? Proceedings of the Third International Workshop on Self-Organizing Systems (IWSOS'08). Karin Anna Hummel and James P. G. Sterbenz, editors. December 10-12, 2008, Vienna, Austria. Volume 5343/2008 of Lecture Notes in Computer Science (LNCS), pages 299–304. ISBN 978-3-540-92156-1, Berlin, Germany: Springer-Verlag GmbH.
    details / doi:10.1007/978-3-540-92157-8_29 / pdf icon pdf / pdf icon pdf
  20. Thomas Weise, Hendrik Skubch, Michael Zapf, and Kurt Geihs. Global Optimization Algorithms and their Application to Distributed Systems. Kasseler Informatikschriften (KIS) 2008, 3, Kassel, Hesse, Germany: University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, September 29, 2008.
    urn:nbn:de:hebis:34-2008101424484
    (includes applications of GP)
  21. Thomas Weise. Internal Cooperation/Brainstorming Session Presentation – Evolving Distributed Algorithms with Genetic Programming. December 15, 2008.
  22. Thomas Weise, Michael Zapf, and Kurt Geihs. Evolving Proactive Aggregation Protocols. In Michael O'Neill, Leonardo Vanneschi, Steven Matt Gustafson, Anna Isabel Esparcia-Alcázar, Ivanoe de Falco, Antonio Della Cioppa, and Ernesto Tarantino, editors, Genetic Programming – Proceedings of the 11th European Conference on Genetic Programming (EuroGP'08), volume 4971/2008 of Lecture Notes in Computer Science (LNCS), pages 254-265, Naples, Italy, March 26–28, 2008. ISBN: 978-3-540-78670-2, Berlin, Germany: Springer-Verlag GmbH.
    details / doi:10.1007/978-3-540-78671-9_22 / pdf icon pdf
  23. Michael Zapf and Thomas Weise. Applicability of Emergence Engineering to Distributed Systems Scenarios. Kasseler Informatikschriften (KIS) 2008, 5, Kassel, Hesse, Germany: University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, January 9, 2009.
    urn:nbn:de:hebis:34-2009010925609
  24. Michael Zapf and Thomas Weise. Offline Emergence Engineering For Agent Societies. The Fifth European Workshop on Multi-Agent Systems (EUMAS'07). December 14, 2007, Hammamet, Tunesia. Also presented at the co-located Fifth Technical Forum Group (TFG5). (both have no formal proceedings)
    details / pdf icon pdf
  25. Thomas Weise, Michael Zapf, and Kurt Geihs. Rule-based Genetic Programming. In Proceedings of the 2nd International Conference on Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS'07), pages 8-15, Budapest, Hungary: Radisson SAS Beke Hotel, December 10–12, 2007. ISBN: 978-963-9799-05-9, Piscataway, NJ, USA: IEEE Computer Society.
    details / doi:10.1109/BIMNICS.2007.4610073 / pdf icon pdf / pdf icon slides / jar icon demo
  26. Michael Zapf and Thomas Weise. Offline Emergence Engineering For Agent Societies. Kasseler Informatikschriften (KIS) 2007, 8, Kassel, Hesse, Germany: University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, December 7, 2007.
    urn:nbn:de:hebis:34-2007120719844
  27. Thomas Weise, Stefan Achler, Martin Göb, Christian Voigtmann, and Michael Zapf. Evolving Classifiers – Evolutionary Algorithms in Data Mining. Kasseler Informatikschriften (KIS) 2007, 4, Kassel, Hesse, Germany: University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, September 28, 2007.
    urn:nbn:de:hebis:34-2007092819260
  28. Thomas Weise, Michael Zapf, Mohammad Ullah Khan, and Kurt Geihs. Genetic Programming meets Model-Driven Development. In Andreas König, Mario Köppen, Ajith Abraham, Christian Igel, and Nikola Kasabov, editors, Proceedings of the 7th International Conference on Hybrid Intelligent Systems (HIS'07), pages 332-335, Kaiserslautern, Germany: Fraunhofer Center FhG ITWM/FhG IESE, September 17–19, 2007. Piscataway, NJ, USA: IEEE Computer Society.
    details / doi:10.1109/HIS.2007.11 / pdf icon pdf / pdf icon poster
  29. Thomas Weise, Michael Zapf, Mohammad Ullah Khan, and Kurt Geihs. Genetic Programming meets Model-Driven Development. Kasseler Informatikschriften (KIS) 2007, 2, Kassel, Hesse, Germany: University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, July 2, 2007.
    urn:nbn:de:hebis:34-2007070218786
  30. Thomas Weise. SIGOA+DGPF: Evolutionary Computation and Genetic Programming for Distributed Computing. August 6, 2007.
  31. Thomas Weise, Kurt Geihs, and Philipp Andreas Baer. Genetic Programming for Proactive Aggregation Protocols. In Bartłomiej Beliczyński, Andrzej Dzieliński, Marcin Iwanowski, and Bernardete Ribeiro, editors, Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA'07), Part I, volume 4431/2007 of Lecture Notes in Computer Science (LNCS), pages 167-173, Warsaw, Poland: Warsaw University of Technology, April 11–17, 2007. ISBN:&nsp;978-3-540-71589-4, Berlin, Germany: Springer-Verlag GmbH.
    details / doi:10.1007/978-3-540-71618-1_19 / pdf icon pdf / pdf icon slides
  32. Thomas Weise, Kurt Geihs, and Philipp Andreas Baer. Genetic Programming for Proactive Aggregation Protocols. In Bartłomiej Beliczyński, Andrzej Dzieliński, Marcin Iwanowski, and Bernardete Ribeiro, editors, Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA'07), Part I, volume 4431/2007 of Lecture Notes in Computer Science (LNCS), pages 167-173, Warsaw, Poland: Warsaw University of Technology, April 11–17, 2007. ISBN:&nsp;978-3-540-71589-4, Berlin, Germany: Springer-Verlag GmbH.
    details / doi:10.1007/978-3-540-71618-1_19 / pdf icon pdf / pdf icon slides
  33. Thomas Weise and Kurt Geihs. Genetic Programming Techniques for Sensor Networks. In Pedro José Marrón, editor, Tagungsband des 5. GI/ITG KuVS Fachgespräch “Drahtlose Sensornetze”, Stuttgart, Germany: Universität Stuttgart, Fakultät 5: Informatik, Elektrotechnik und Informationstechnik, Institut für Parallele und Verteilte Systeme (IPVS), July 17–18, 2006, pages 21-25. Proceedings published as Technical Report TR-2006-07 of Stuttgart, Germany: University of Stuttgart, Computer Science Faculty.
    details / pdf icon pdf / pdf icon slides / proceedings
  34. Thomas Weise and Kurt Geihs. DGPF – An Adaptable Framework for Distributed Multi-Objective Search Algorithms Applied to the Genetic Programming of Sensor Networks. In Bogdan Filipič and Jurij Šilc, editors, Proceedings of the Second International Conference on Bioinspired Optimization Methods and their Applications (BIOMA'06), pages 157-166, Informacijska Družba (Information Society) / Ljubljana, Slovenia: Jožef Stefan International Postgraduate School, October 9–10, 2006. Ljubljana, Slovenia: Jožef Stefan Institute.
    details / pdf icon pdf / pdf icon pdf / proceedings
  35. Thomas Weise. Genetic Programming for Sensor Networks. Technical Report, Kassel, Hesse, Germany: University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, Distributed Systems Group, January 2006.

Optimization in General

Besides doing research on concrete problems, I always tried to obtain a general understanding of optimization and to turn this understanding into contributions to the field. For instance, I began to summarize everything I learned in a document for myself, which turned into an e-book and became my most-cited publication. Together with valued colleagues, I wrote some texts in which we discuss the features that make an optimization problem hard (most of which are present in the kind of Genetic Programming I was working on as PhD student). I also contributed some ideas how to tackle at least some of them in .

Real-World Applications of Optimization

I would, of course, be interested to apply my research real-world problems and to make an actual difference outside, in the real world. I had the incredible luck to contribute to the in.west project for logistic planning. Furthermore, I co-edited a book on evolutionary algorithms for real-world applications.