Sequential experimentation by evolutionary algorithms
暂无分享,去创建一个
[1] A. E. Eiben,et al. From evolutionary computation to the evolution of things , 2015, Nature.
[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] 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.
[4] Joshua D. Knowles,et al. 'Hang On a Minute': Investigations on the Effects of Delayed Objective Functions in Multiobjective Optimization , 2013, EMO.
[5] Joseph G. Pigeon,et al. Statistics for Experimenters: Design, Innovation and Discovery , 2006, Technometrics.
[6] Ofer M. Shir,et al. Accelerated optimization and automated discovery with covariance matrix adaptation for experimental quantum control , 2009 .
[7] Günter Rudolph,et al. Contemporary Evolution Strategies , 1995, ECAL.
[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] Ingo Rechenberg,et al. Case studies in evolutionary experimentation and computation , 2000 .
[10] Ofer M. Shir,et al. Quantum control experiments as a testbed for evolutionary multi-objective algorithms , 2012, Genetic Programming and Evolvable Machines.
[11] Joshua D. Knowles,et al. On Handling Ephemeral Resource Constraints in Evolutionary Search , 2013, Evolutionary Computation.
[12] Joshua D. Knowles. Closed-loop evolutionary multiobjective optimization , 2009, IEEE Computational Intelligence Magazine.