Prof. Ofer Shir, Michal Horovitz : Computational Intelligence
Bayesian Performance Analysis for Black-Box Optimization Benchmarking.
Calvo, B., Shir, O.M., Ceberio, J., Doerr, C., Wang, H., Bäck, T., Lozano, J.A.
2019
,
In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 1789?1797
Predict or Screen Your Expensive Assay? DoE vs. Surrogates in Experimental Combinatorial Optimization.
Horesh, N., Bäck, T., Shir, O.M.
2019
,
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 274?284
Statistical Learning in Soil Sampling Design Aided by Pareto Optimization.
Israeli, A., Emmerich, M., Litaor, M., Shir, O.M.
2019
,
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 1198?1205
Compiling A Benchmarking Test-Suite For Combinatorial Black-Box Optimization: A Position Paper.
Shir, O.M., Doerr, C., Bäck, T.
2018
,
In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO-2018 ,2018, ACM Press Ed. , New York, NY, USA,, Pages 1753?1760
Evolution Strategies.
Emmerich, M.T.M., Shir, O.M., Wang, H.
2018
,
In Handbook of Heuristics. ,2018, Martí R., Panos P., Resende M. Ed. ,Springer Cham , Pages 1-31
Solving Structures of Pigment-Protein Complexes as Inverse Optimization Problems using Decomposition.
Lahav, Y., Shir, O.M., Noy, D.
2017
,
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2017, ,2017, Ed. ,ACM Press New York, NY, USA,, Pages 1169?1176
On the Statistical Learning Ability of Evolution Strategies
Shir, O.M., Yehudayoff, A.
2017
,
In Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms FOGA-2017 ,2017, ACM Press Ed. ,ACM Press New York, NY, US, Pages 127?138
Protein Design by Multiobjective Optimization: Evolutionary and Non-Evolutionary Approaches.
Belure, S.V., Shir, O.M., Nanda, V.
2017
,
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2017 ,2017, Ed. ,ACM Press New York, USA, Pages 1081?1088
Searching for the Pareto frontier in multi-objective protein design.
Nanda, V., Belure, S.V., Shir, O.M.
2017
,
Biophysical Reviews 2017 Volume 9 Issue 4 Pages 339?344
On the Capacity of Evolution Strategies to Statistically Learn the Landscape.
Shir, O.M., Roslund, J., Yehudayoff, A.
2016
,
Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2016, 2016 Pages 151-152
Efficient Isothermal Titration Calorimetry Technique Identifies Direct Interaction of Small Molecule Inhibitors with the Target Protein.
Gal, M., Bloch I., Shechter, N., Romanenko, O., Shir, O.M.
2016
,
Comb Chem High Throughput Screen 2016 Volume 19 Issue 1 Pages 4-13
Genetic background and environmental conditions drive metabolic variation in wild type and transgenic soybean (Glycine max) seeds
Cohen H, Shir OM, Yu Y, Hou W, Sun S, Han T, Amir R.
2016
,
Plant Cell and Environment 2016 Volume 39 Pages 1805-17
Multilevel Evolution Strategies for Multigrid Problems.
Shir, O.M.
2016
,
ACM Press New York, NY, USA, ,2016
Exploring the complexity of quantum control optimization trajectories.
Nanduri, A., Shir, O.M., Donovan, A., Ho, T.-S., Rabitz, H.
2015
,
Physical Chemistry Chemical Physics 2015 Volume 17 Issue 1 Pages 334?347
Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning
Shir, O.M., Roslund, J., Whitley, D., Rabitz, H.
2014
,
Physical Review E 2014 Volume 89(6) 063306
Pareto Landscapes Analyses via Graph-Based Modeling for Interactive Decision-Making.
Shir, O.M., Chen, Sh., Amid, D., Margalit, O., Masin, M., Anaby-Tavor, A., Boaz, D.
2014
,
In EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. Advances in Intelligent Systems and Computing ,2014, Tantar AA. et al. Ed. ,Springer, Cham , Pages 97?113
Self-Organizing Maps for Multi-Objective Pareto Frontiers.
Chen, Sh., Amid, D., Shir, O.M., Boaz, D., Schreck, T., Limonad, L.
2013
,
In Proceedings of the Pacific Visualization Symposium, PacificVis-2013 ,2013, IEEE Ed. ,IEEE Pacific , Pages 153?160
Pareto Optimization and Tradeoff Analysis Applied to Meta-Learning of Multiple Simulation Criteria.
Shir, O.M., Moor, D., Chen, Sh., Amid, D., Boaz, D., Anaby-Tavor, A.
2013
,
In Proceedings of the 2013 Winter Simulation Conference (INFORMS WSC-2013) ,2013, Ed. ,IEEE , Pages 89?100
Algorithms for Finding Maximum Diversity of Design Variables in Multi-Objective Optimization.
Zadorojniy, A., Masin, M., Greenberg, L., Shir, O.M., Zeidner, L.
2012
,
In Conference on Systems Engineering Research. Volume 8 of Procedia Computer Science. ,2012, Ed. ,Elsevier , Pages 171-176
Niching in Evolutionary Algorithms
Shir, O.M.
2012
,
Handbook of Natural Computing: Theory, Experiments, and Applications 2012 Pages 1035?1069
Quantum Control Experiments as a Testbed for Evolutionary Multi-Objective Algorithms
Shir, O.M., Roslund, J., Leghtas, Z., Rabitz, H.
2012
,
Genetic Programming and Evolvable Machines 2012 Volume 13 Issue 4 Pages 445?491
Control of Nirtomethane Photoionization Efficiency with Shaped Femtosecond Pulses.
Roslund, J., Shir, O.M., Dogariu, A., Miles, R., Rabitz, H.
2011
,
The Journal of Chemical Physics 2011 Volume 134 Issue 15 Pages 154301
Evolutionary Optimization of Rotational Population Transfer.
Rouzée, A., Ghafur, O., Vidma, K., Gijsbertsen, A., Shir, O.M., Bäck, T., Meijer, A., van der Zande, W.J., Parker, D., Vrakking, M.J.J.
2011
,
Physical Review A 2011 Volume 84 Issue 3 Pages 033415
Fidelity Between Unitary Operators and the Generation of Gates, Robust Against Off-Resonance Perturbations.
Cabrera, R., Shir, O.M., Wu, R., Rabitz, H.
2011
,
Journal of Physics A: Mathematical and Theoretical 2011 Volume 44 Issue 9 Pages 095302
Multiobjective Adaptive Feedback Control of Two-Photon Absorption Coupled with Propagation through a Dispersive Medium.
Laforge, F.O., Roslund, J., Shir, O.M., Rabitz, H.
2011
,
Physical Review A 2011 Volume 84 Issue 1 Pages 013401