Sequential experimentation by evolutionary algorithms

[1]  Ofer M. Shir,et al.  Quantum control experiments as a testbed for evolutionary multi-objective algorithms , 2012, Genetic Programming and Evolvable Machines.

[2]  Douglas B Kell,et al.  Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments? , 2012, BioEssays : news and reviews in molecular, cellular and developmental biology.

[3]  Joshua D. Knowles,et al.  On Handling Ephemeral Resource Constraints in Evolutionary Search , 2013, Evolutionary Computation.

[4]  A. E. Eiben,et al.  From evolutionary computation to the evolution of things , 2015, Nature.

[5]  Weiru Liu,et al.  A survey of formalisms for representing and reasoning with scientific knowledge , 2010, The Knowledge Engineering Review.

[6]  Joseph G. Pigeon,et al.  Statistics for Experimenters: Design, Innovation and Discovery , 2006, Technometrics.

[7]  L. D. Whitley,et al.  Efficient retrieval of landscape Hessian: forced optimal covariance adaptive learning. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Joshua D. Knowles,et al.  ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems , 2006, IEEE Transactions on Evolutionary Computation.

[9]  Joshua D. Knowles Closed-loop evolutionary multiobjective optimization , 2009, IEEE Computational Intelligence Magazine.

[10]  Ofer M. Shir,et al.  Accelerated optimization and automated discovery with covariance matrix adaptation for experimental quantum control , 2009 .

[11]  Judea Pearl,et al.  The seven tools of causal inference, with reflections on machine learning , 2019, Commun. ACM.

[12]  Günter Rudolph,et al.  Contemporary Evolution Strategies , 1995, ECAL.

[13]  Ingo Rechenberg,et al.  Case studies in evolutionary experimentation and computation , 2000 .