Homepage for Marcus Furuholmen

Marcus Furuholmen Ph.D Candidate/Senior Engineer Cellphone: +47 920 15 802 Email: marcusfu@ifi.uio.no Email: marcus.furuholmen@akersolutions.no

I am currently doing a PhD as part of the TAIL IO project; a joint industry research project between Aker Solutions, ABB, IBM, SKF and StatoilHydro. My research is focused on the use of optimization and machine learning applied to problems in the oil and gas industry. I am working on problems such as automated system identification, classification of sensor data, design automation, control, planning and scheduling. Methods that I use are evolutionary computation (EA) by means gene expression programming (GEP) and learning classifier systems (LCS). Of special interests are scalability problems, coevolution and multi-objective optimization.

Curriculum

PhD Candidate, University of Oslo (UiO), Department of Informatics, Robotics and Intelligent Systems Group (ROBIN) , Norway

Senior Engineer, Aker Subsea AS, Systems Engineering, Norway

Product Design Engineer, Hareide Designmill AS, Norway

Product Design Engineer (Internship), Continuum Design, Milan, Italy

M.Sc Product Design Engineering, Norwegian University of Science and Technology (NTNU), Norway

Refereed Conference Publications

Furuholmen M., Glette K., Hovin M., Torresen J., Scalability, Generalization and Coevolution - Experimental Comparisons Applied to Automated Facility Layout Planning, The Genetic and Evolutionary Computation Conference (GECCO-2009) Nominated for best paper award

Furuholmen M., Glette K., Hovin M., Torresen J., Coevolving Heuristics for The Distrbutor's Pallet Packing Problem 2009 IEEE Congress on Evolutionary Computation (IEEE CEC 2009)

Furuholmen M., Glette K., Hovin M., Torresen J., Continuous Adaptation in Robotic Systems by Indirect Online Evolution The 8'th International Conference on Evolvable Systems: From Biology to Hardware (ICES)

Furuholmen M., Glette K., Hovin M., Torresen J., Indirect Online Evolution - a Conceptual Framework for Adaptation in Industrial Robotic Systems The 2008 ECSIS Symposium on Learning and Adaptive Behavior in Robotic Systems (Lab RS)

Vasicek Zdenek, Bidlo Michal, Sekanina Lukas, Torresen Jim, Glette Kyrre, Furuholmen Marcus: Evolution of Impulse Bursts Noise Filters, In: Proc. of the 2009 NASA/ESA Conference on Adaptive Hardware and Systems, Los Alamitos, US, IEEE CS, 2009, p. 27-34, ISBN 978-0-7695-3714-6