single faculty member

  • Dr.

    Ofer Shir
  • Senior Lecturer

  • Computer Science - B.Sc. Program, Computer Science M.Sc., Water Sciences M.SC
  • I am interested in learning and optimization questions related to systems within the Natural Sciences. Specifically, my fields of interest encompass Statistical Learning in Theory and in Practice, Experimental Optimization, Theory of Randomized Search Heuristics, Scientific Informatics, Natural Computing, Computational Intelligence in Physical Sciences, Quantum Control and Quantum Information. 

  • Currently:
    * Senior Lecturer (Assistant Professor), Department of Computer Science, Tel-Hai College.
    * Principal Investigator, Computational Sciences, Migal-The Galilee Research Institute.

    * Lecturer, Department of Computer Science, Tel-Hai College.
    * Research Scientist, IBM-Research.
    * Postdoctoral Research Associate, Princeton University. Host: Hersch Rabitz.
    * MSc and PhD in Computer Science, Leiden University. Advisors: Thomas Bäck and Marc Vrakking.
    * BSc in Physics and Computer Science, Hebrew University of Jerusalem.

  • Migal research group:


    • The C++ Programming Language (121503; Fall)
    • Introduction to Computational Intelligence (199811; Fall)
    • Advanced Topics in Object-Oriented Programming (199414; Spring)


  • 2004-2008: FOM PhD Scholarship, AMOLF / Leiden University, The Netherlands


    Shir, O.M., Yehudayoff, A.: On the Statistical Learning Ability of Evolution Strategies. In: Proceedings of the workshop on Foundations of Genetic Algorithms, FOGA-2017, New York, NY, USA, ACM Press (2017) 127-138

    Nanda, V., Belure, S.V., Shir, O.M.: Searching for the Pareto frontier in multi-objective protein design. Biophysical Reviews 9(4) (2017) 339—344

    Nanduri, A., Shir, O.M., Donovan, A., Ho, T.-S., Rabitz, H.: Exploring the complexity of quantum control optimization trajectories. Physical Chemistry Chemical Physics 17(1) (2015) 334—347

    Shir, O.M., Roslund, J., Whitley, D., Rabitz, H.: Efficient Retrieval of Landscape Hessian: Forced Optimal Covariance Adaptive Learning. Physical Review E 89(6) (2014) 063306

    Zadorojniy, A., Masin, M., Greenberg, L., Shir, O.M., Zeidner, L.: Algorithms for Finding Maximum Diversity of Design Variables in Multi-Objective Optimization. In: Conference on Systems Engineering Research. Volume 8 of Procedia Computer Science., Elsevier (2012) 171-176

    Shir, O.M., Roslund, J., Leghtas, Z., Rabitz, H.: Quantum Control Experiments as a Testbed for Evolutionary Multi-Objective Algorithms. Genetic Programming and Evolvable Machines 13(4) (2012) 445—491 

    Shir, O.M.: Niching in Evolutionary Algorithms. In: Handbook of Natural Computing: Theory, Experiments, and Applications. Springer-Verlag, Berlin-Heidelberg, Germany (2012) 1035—1069

    Shir, O.M., Emmerich, M., Bäck, T.: Adaptive Niche-Radii and Niche-Shapes Approaches for Niching with the CMA-ES. Evolutionary Computation 18(1) (2010) 97–126

    Shir, O.M., Beltrani, V., Bäck, T., Rabitz, H., Vrakking, M.J.: On the Diversity of Multiple Optimal Controls for Quantum Systems. Journal of Physics B: Atomic, Molecular and Optical Physics 41(7) (2008) 074021

    Shir, O.M., Raz, V., Dirks, R.W., Bäck, T.: Classification of Cell Fates with Support Vector Machine Learning. In: Proceedings of EvoBIO 2007. Volume 4447 of Lecture Notes in Computer Science., Springer (2007) 258–269



  • Full CV download
    • Mathematical Programming as a Complement to Bio-Inspired Optimization (PPSN'18 tutorial; Coimbra PORTUGAL, September 2018) pdf
    • Introductory mathematical programming for EC (GECCO'18 tutorial; Kyoto JAPAN, July 2018) ACM/dL
    • Sequential experimentation by evolutionary algorithms (GECCO'18 joint tutorial with Thomas Bäck; Kyoto JAPAN, July 2018) ACM/dL
    • On the Statistical Learning Ability of Evolution Strategies (Foundations of Genetic Algorithms Workshop FOGA-XIV, January 2017) pdf
    • Computational Intelligence in the Natural Sciences: Machine Learning, Optimization, and Heuristic Search (Princeton University Seminar, July 2016) pdf
    • Pareto Automated Recommendation (LIACS Colloquium, Dec. 2014) pdf