We are interested in learning and optimization questions related to systems within the Natural Sciences. Our group is especially interested in Machine/Statistical Learning and Global Optimization aspects of experimental domains, that is, practical physical-, life- and agro-sciences search-problems whose simulated models are unavailable and thus require real-world experimentation.
Benchmarking Discrete Optimization Heuristics with IOHprofiler.
Applied Soft Computing 88 (2020) 106027
On the Covariance-Hessian Relation in Evolution Strategies.
Theoretical Computer Science 801 (2020) 157—174
Predict or Screen Your Expensive Assay? DoE vs. Surrogates in Experimental Combinatorial Optimization.
In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 274?284
Bayesian Performance Analysis for Black-Box Optimization Benchmarking.
In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO-2019 ,2019, Ed. ,ACM Press New York, NY, USA, Pages 1789?1797
Research Group Leader
Prof. Ofer Shir