Roland Olsson

• Nguyen, Hoa Thi; Olsson, Roland & Haugen, Øystein (2024). Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). ISSN 2644-1284. 5, s. 91–108. doi: 10.1109/OJIES.2024.3363500.
• Nguyen, Hoa Thi; Haugen, Øystein & Olsson, Roland (2023). Imitation Learning from Operator Experiences for a Real time CNC Machine Controller. IEEE Conference on Industrial Informatics. ISSN 1935-4576. doi: 10.1109/INDIN51400.2023.10218164. Fulltekst i vitenarkiv
• Magnusson, Lars Vidar; Olsson, Roland & Tran, Chau Thi Thuy (2023). Recurrent Neural Networks for Oil Well Event Prediction. IEEE Intelligent Systems. ISSN 1541-1672. 38(2), s. 73–80. doi: 10.1109/MIS.2023.3252446. Fulltekst i vitenarkiv
• Geitle, Marius & Olsson, Roland (2019). A New Baseline for Automated Hyper-Parameter Optimization. I Nicosia, Giuseppe; Umeton, Renato; Sciacca, Vincenzo; Pardalos, Panos & Giuffrida, Giovanni (Red.), Machine Learning,Optimization,and Data Science - 5th International Conference, LOD 2019. Springer Nature. ISSN 978-3-030-37598-0. s. 521–530. doi: https:/doi.org/10.1007/978-3-030-37599-7_43.
• Brastein, Ole Magnus; Olsson, Roland; Skeie, Nils-Olav & Lindblad, Thomas (2017). Human Activity Recognition by machine learning methods. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Vis sammendrag

  Recognition of human activity from sensor data is a research field of great potential. Giving autonomous systems the ability to identify what a human subject is doing at a given time is highly useful in many industries, particularly in health care and security monitoring. Our results, using a public domain dataset, show that the state- of-the-art decision tree ensemble algorithm XGBoost gives an accuracy of 94.6% validated on an independent test set. Previously published results using support vector machines (SVM) gave an accuracy of 90.2%. As far as we know, our result is the new state of the art for this data set. Recognition of human activity carries potential privacy concerns, which to some degree constrain the choice of sensor technology. Therefore, systems such as ours which can identify activities from simple inertial sensors, e.g. accelerators and gyroscopes are of particular interest. Data from such inertial sensors are difficult to interpret using mechanistic models; hence the field of Machine Learning is particularly interesting for this application.

• Dang-Ha, The-Hien; Roverso, Davide & Olsson, Roland (2017). Graph of virtual actors (GOVA): A Big Data analytics architecture for IoT. I IEEE, . (Red.), 2017 IEEE International Conference on Big Data and Smart Computing (BigComp): Proceedings of a meeting held 13-16 February 2017, Jeju Island, South Korea. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-3015-6. s. 162–169. doi: 10.1109/BIGCOMP.2017.7881733.
• Geitle, Marius & Olsson, Roland (2017). Using automatic programming to design improved variants of differential evolution. I Bui, Lam Thu (Red.), 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-0743-5. s. 13–18. doi: 10.1109/IESYS.2017.8233554.
• Geitle, Marius & Olsson, Roland (2017). Improving competitive differential evolution using automatic programming. I Fei, Xiang (Red.), 2017 4th International Conference on Systems and Informatics. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-1107-4. s. 538–545. doi: 10.1109/ICSAI.2017.8248350.
• Dang Ha The, Hien; Bianchi, Filippo Maria & Olsson, Roland (2017). Local short term electricity load forecasting: Automatic approaches. Proceedings of the International Joint Conference on Neural Networks. ISSN 2161-4393. s. 4267–4274. doi: 10.1109/IJCNN.2017.7966396.
• Magnusson, Lars Vidar & Olsson, Roland (2016). Improving the Canny Edge Detector Using Automatic Programming: Improving Hysteresis Thresholding. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. doi: 10.1109/icivc.2016.7571270.
• Magnusson, Lars Vidar & Olsson, Roland (2016). Improving the Canny edge detector using automatic programming: Improving non-max suppression, GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016. Association for Computing Machinery (ACM). ISSN 978-1-4503-4206-3. s. 461–468. doi: 10.1145/2908812.2908926.
• Magnusson, Lars Vidar & Olsson, Roland (2016). Improving the Canny edge detector using automatic programming: Improving the filter, 2016 International Conference on Image, Vision and Computing (ICIVC 2016): August 3-5, 2016: Portsmouth, UK. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-3755-1. s. 36–40. doi: 10.1109/ICIVC.2016.7571270.
• Dang, Ha The Hien; Olsson, Roland & Wang, Hao (2016). Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler - Norway. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713.
• Dang, Ha The Hien; Olsson, Roland & Wang, Hao (2015). The Role of Big Data on Smart Grid Transition. I Liu, Xingang; Hsu, Robert C.; Wang, Peicheng; Xia, Feng; Wang, Yufeng; Dong, Mianxiong & Deng, Yuhui (Red.), Proceedings of 2015 IEEE International Conference on Smart City (SmartCity 2015). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781509018925. s. 33–39. doi: 10.1109/SmartCity.2015.43.
• Larsen, Kristin; Magnusson, Lars Vidar & Olsson, Roland (2014). Edge Pixel Classification Using Automatic Programming. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713.
• Magnusson, Lars Vidar & Olsson, Roland (2014). Improving Graph-Based Image Segmentation Using Automatic Programming. I Esparcia-Alcázar, Anna I. & Mora, Antonio (Red.), Applications of Evolutionary Computation. 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014. Springer. ISSN 978-3-662-45523-4. s. 464–475. doi: 10.1007/978-3-662-45523-4_38.
• Olsson, Roland & Løkketangen, Arne (2013). Using automatic programming to generate state-of-the-art algorithms for random 3-SAT. Journal of Heuristics. ISSN 1381-1231. 19(5), s. 819–844. doi: 10.1007/s10732-013-9226-x. Vis sammendrag

  The paper focuses on automatic programming and how to synthesize stochastic local search algorithms using automatic design of algorithms through evolution (ADATE). The main goal is to provide support for the hypothesis that automatic programming can generate competitive heuristic algorithms. A well studied and highly optimized SAT solver, G2WSAT, is used as a case study. The results indicate that automatic programming is an effective design tool for heuristics and that there may be many well studied optimization problems that could benefit from the ADATE system and the specification methodology that we describe.

• Olsson, Roland & Vu, Huyen Phuong (2012). Automatic improvement of graph based image segmentation. I Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; Fowlkes, Charless; Wang, Sen; Choi, Min-Hyung; Mantler, Stephan; Schulze, Jürgen; Acevedo, Daniel; Mueller, Klaus & Papka, Michael (Red.), Advances in Visual Computing; 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers, Part I. Springer. ISSN 9783642331787. s. 578–587. doi: 10.1007/978-3-642-33191-6_57.
• Olsson, Roland & Løkketangen, Arne (2011). Improving State-of-the-Art 3-SAT solvers using Automatic Design of Algorithms through Evolution. I Hao, Jin-Kao; Legrand, Pierrick; Collet, Pierre; Monmarché, Nicolas; Lutton, Evelyne & Schoenauer, Marc (Red.), Artificial Evolution 2011 (Evolution Artificielle 2011): 10th Biennal International Conference on Artificial Evolution: proceedings. Université d’Angers. ISSN 978-2-9539267-1-2. s. 192–197. Vis sammendrag

  Automatic generation of code is becoming an increasingly important field. This also includes code for combinatorial optimization. We show how such an automated code generation system, ADATE, is able to improve significantly on a state-of-the-art SAT-solver, G2WSAT, by automatically evolving and improving the original program in a controlled fashion. We have chosen SAT - the Satisfiability Problem - as our target problem, as this is a central problem in many real-world applications, and because improving SAT-solvers might have significant economic impact. Our computational testing clearly shows the code improvement, as well as showing that the resulting code can be generalized and used outside its original learning domain.

• Løkketangen, Arne & Olsson, Roland (2010). Generating meta-heuristic optimization code using ADATE. Journal of Heuristics. ISSN 1381-1231. 16(6), s. 911–930. doi: 10.1007/s10732-009-9119-1. Vis sammendrag

  Local Search based meta-heuristic methods for finding good solutions to hard combinatorial optimization problems have attained a lot of success, and a plethora of methods exist, each with its own successes, and also with its own parameter settings and other method-specific details. At the same time, experience is needed to implement highly competitive code, and some of the experience applied is not easy to quantify. ADATE is a system to automatically generate code based on a set of input-output specifications, and can work in vastly different domains. It generates code in a subset of the programming language ML and works by searching for transformations of purely functional ML programs. Code automatically generated by the ADATE system compares with state-of-the-art handcrafted meta-heuristic optimization code. In particular, the programs generated by ADATE target the move selection part of BOOP - Boolean Optimization Problems. The baseline is a highly successful Tabu Search implementation. Comparisons are made for versions running for a limited number of iterations, being suitable for applications needing a short response time. The computational results show that the ADATE system is able to generate highly competitive code that produces more optimal solutions to hard BOOP instances within given iteration limits than the previously published Tabu Search implementation. The automatically generated code also gives new insights into the general design of meta-heuristic mechanisms, and contains novel search mechanisms.

• Chilo, José; Horvath, György; Lindblad, Thomas; Olsson, Roland; Redeby, Johan & Roeraade, Johan (2009). A flexible electronic nose for odor discrimination using different methods of classification, 16th IEEE-NPSS Real Time Conference, 2009 : RT '09. IEEE conference proceedings. ISSN 9781424444540. s. 317–320.
• Chilo, José; Horvath, György; Lindblad, Thomas & Olsson, Roland (2009). Electronic nose ovarian carcinoma diagnosis based on machine learning algorithms, Advances in data mining : applications and theoretical aspects. Springer. ISSN 9783642030666. s. 13–23.
• Berg, Henrik; Olsson, Roland; Rusås, Per-Olav & Jakobsen, Morgan (2009). Automated synthesis of control algorithms from first principles, Proceedings IEEE International Conference on Intelligent Robots and Systems. IEEE conference proceedings. ISSN 978-1-4244-3803-7. s. 2958–2965.
• Berg, Henrik; Rusås, Per-Olav; Olsson, Roland & Jakobsen, Morgan (2009). Synthesis of control algorithms for autonomous vehicles through automatic programming. I Wang, Haiying; Low, Kai Soon; Wei, Kexin & Sun, Junqing (Red.), Fifth International Conference on Natural Computation, ICNC 2009. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-0-7695-3736-8. s. 445–453.
• Olsson, Roland; Berg, Henrik; Lindblad, Thomas & Chilo, Jose (2008). Automatic design of pulse coupled neurons for image segmentation. Neurocomputing. ISSN 0925-2312. 71(10.des), s. 1980–1993. Vis sammendrag

  Automatic Design of Algorithms through Evolution (ADATE) is a program synthesis system that creates recursive programs in a functional language with automatic invention of recursive help functions and self-adaptive optimization of numerical values. We implement a neuron in a pulse coupled neural network (PCNN) as a recursive function in the ADATE language and then use ADATE to automatically evolve better PCNN neurons for image segmentation. Our technique is generally applicable for automatic improvement of most image processing algorithms and neural computing methods. It may be used either to generally improve a given implementation or to tailor that implementation to a specific problem, which with respect to image segmentation for example can be road following for autonomous vehicles or infrared image segmentation for heat seeking missiles that are to distinguish the heat source of the target from flares.

• Olsson, Roland; Hansen, Stig-Erland; Chilo, Jose & Lindblad, Thomas (2007). Classification of infrasound events with various machine learning techniques, Proceedings from the 4th International Conference on Cybernetics and Information Technologies, Systems and Applications : CITSA 2007. International Institute of Informatics and Systemics.
• Olsson, Roland (2007). Automatic design of algorithms through evolution, Proceedings of the Workshop on Approaches and Applications of Inductive programming (AAIP '07). Faculty of Information Systems and Applied Computer Science.
• Olsson, Roland; Hansen, Stig-Erland; Lindblad, Thomas & Chilo, Jose (2007). Comparison of three feature extraction techniques to distinguish between different infrasound signals, Progress in pattern recognition. Springer. ISSN 1617-7916. s. 75–82.
• Olsson, Roland & Hansen, Stig-Erland (2007). Improving decision tree pruning through automatic programming, NIK 2007 : Norsk informatikkonferanse. Tapir Akademisk Forlag. s. 31–40.
• Berg, Henrik & Olsson, Roland (2005). Improving the Scalability of Automatic Programming. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. Vis sammendrag

  When automatically constructing large programs using program transformations, the number of possible transformations grows very fast. In this paper, we introduce and test a new way of combining several program transformations into one transformation, inspired by the combinatorial concept of Covering Arrays (CA). We have equipped the ADATE automatic programming system with this new CA transformation and conducted a series of 18 experiments which show that the CA transformation is a highly useful supplement to the existing ADATE transformations.

• Olsson, Roland (2003). Machine learning of human language through automatic programming. ?. s. 18–22.
• Olsson, Roland & Powers, David (2003). Learning human language semantics through inductive functional programming, Eighth Scandinavian Conference on Artificial Intelligence : SCAI'03. IOS Press. s. 131–140.
• Olsson, Roland (2000). Neural Programming. HØit : tidsskrift for Avdeling for informasjonsteknologi, Høgskolen i Østfold. ISSN 0805-6692.
• Olsson, Roland & Montelius, Gunnar (1998). Låt datorn Skriva Programmen! ?. s. 22–23. Vis sammendrag

  R. Olsson har utviklet et system for automatiske programmering, ADATE, som betyr Automatic Design of Algorithms through Evolution. Artikkelen gir n eksempler på hvordan Novisen, Proffset og Forskaren kan andvende ADATE.

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• Nguyen, Hoa Thi; Haugen, Øystein & Olsson, Roland (2023). Imitation Learning from Operator Experiences for a Real time CNC Machine Controller.
• Olsson, Roland & Acharya, Shubodha (2022). Using Automatic Programming to Improve Gradient Boosting for Classification.
• Geitle, Marius & Olsson, Roland (2019). A New Baseline for Automated Hyper-Parameter Optimization.
• Brastein, Ole Magnus; Olsson, Roland; Skeie, Nils-Olav & Lindblad, Thomas (2017). Human Activity Recognition by machine learning methods. Vis sammendrag

  Recognition of human activity from sensor data is a research field of great potential. Giving autonomous systems the ability to identify what a human subject is doing at a given time is highly useful in many industries, particularly in health care and security monitoring. Our results, using a public domain dataset, show that the state- of-the-art decision tree ensemble algorithm XGBoost gives an accuracy of 94.6% validated on an independent test set. Previously published results using support vector machines (SVM) gave an accuracy of 90.2%. As far as we know, our result is the new state of the art for this data set. Recognition of human activity carries potential privacy concerns, which to some degree constrain the choice of sensor technology. Therefore, systems such as ours which can identify activities from simple inertial sensors, e.g. accelerators and gyroscopes are of particular interest. Data from such inertial sensors are difficult to interpret using mechanistic models; hence the field of Machine Learning is particularly interesting for this application.

• Geitle, Marius & Olsson, Roland (2017). Using automatic programming to design improved variants of differential evolution.
• Geitle, Marius & Olsson, Roland (2017). Improving competitive differential evolution using automatic programming.
• Olsson, Roland & Vu, Huyen (2012). Automatic Improvement of Graph Based Image Segmentation.
• Løkketangen, Arne & Olsson, Roland (2010). Generating Meta-Heuristic Optimization Code using ADATE.
• Chilo, José; Horvath, György; Lindblad, Thomas & Olsson, Roland (2009). Electronic nose ovarian carcinoma diagnosis based on machine learning algorithms.
• Berg, Henrik; Olsson, Roland; Rusås, Per-Olav & Jakobsen, Morgan (2009). Automated synthesis of control algorithms from first principles.
• Berg, Henrik; Olsson, Roland; Rusås, Per-Olav & Jakobsen, Morgan (2009). Synthesis of control algorithms for autonomous vehicles through automatic programming.
• Chilo, José; Horvath, György; Lindblad, Thomas; Olsson, Roland; Redeby, Johan & Roeraade, Johan (2009). A flexible electronic nose for odor discrimination using different methods of classification.
• Løkketangen, Arne & Olsson, Roland (2009). Generating meta-heuristic optimization code using ADATE.
• Løkketangen, Arne & Olsson, Roland (2009). Generating metaheuristic optimization code using ADATE.
• Olsson, Roland; Hansen, Stig-Erland; Lindblad, Thomas & Chilo, Jose (2007). Comparison of three feature extraction techniques to distinguish between different infrasound signals.
• Olsson, Roland & Hansen, Stig-Erland (2007). Improving decision tree pruning through automatic programming.
• Olsson, Roland; Hansen, Stig-Erland; Chilo, Jose & Lindblad, Thomas (2007). Classification of infrasound events with various machine learning techniques.
• Olsson, Roland (2007). Automatic design of algorithms through evolution (ADATE).
• Olsson, Roland & Jakobsen, Morgan (2006). Generation of a four-way item-to-item navigation algorithm using automatic programming. ?. s. 9–13.
• Olsson, Roland & Jakobsen, Morgan (2006). A look at generation of user interface behaviour using automatic programming. WSEAS Transactions on Information Science and Applications. ISSN 1790-0832. 3(10), s. 1909–1915. Vis sammendrag

  With the rising popularity of mobile devices, applications for these devices must meet the challenges of a system with keypad input only. Navigating between randomly placed items using four directional keys can be frustrating, if the navigation does not meet the users intuitive expectations. Automatic programming with the ADATE system is used to generate a navigation algorithm based on subjective inputs from a user. This means that the user's mental model of navigation automatically is translated into a program.

• Berg, Henrik & Olsson, Roland (2005). Improving the Scalability of Inductive Functional Programming. ?. 4(8), s. 837–848. Vis sammendrag

  When automatically synthesizing big programs, it is important to consider the combinatorial properties of the program transformations that are employed. In this paper, we show how to effectively generate transformation sequences representing incremental changes to a given program. In order to cover combinations of transformations, we use so-called covering arrays in which each line is a transformation sequence to be translated into a program. Covering and orthogonal arrays are standard tools from combinatorial design theory to guarantee coverage of all pair-wise parameter combinations, for example. We equipped the ADATE automatic programming system with our new covering array (CA) transformation and conducted a series of 18 experiments which show that the CA transformation is a highly useful supplement to the old ADATE transformations.

• Olsson, Roland & Powers, David (2003). Machine Learning of Human Language through Automatic Programming.
• Olsson, Roland (2002). Self-Improving Genetic Programming.
• Olsson, Roland (2001). Neural programming : automatic synthesis of symbolic programs using numerical optimization. Vis sammendrag

  This paper shows how symbolic programs can be extracted from a special kind of recurrent neural networks, called neural progr ms. The technique is based on transforming between ordinary discrete data structures and ``continuous'' data structures representing probability distributions. The linear combinations of real values in ordinary neural networks are replaced with mi ing of continuous data structures. Node activation functions, such as the sigmoid and tanh functions, are replaced with continuous equivalents of normal functions in symbolic programs, for example sorting a list. The resulting nets can use very ma y hidden layers since these new activation functions turn out to be smoother than for example the sigmoid.

• Olsson, Roland & Ricardo, Poli (2000). How to Invent Functions.
• Olsson, Roland (1999). Population management for Automatic Design of Algorithms Through Evolution.
• Olsson, Roland (1999). The Art of Writing Specifications for the ADATE Automatic Programming System.

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