A new look at Bellman's principle of optimality
Journal of Optimization Theory and Applications
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Information-based objective functions for active data selection
Neural Computation
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Probabilistic robustness analysis: explicit bounds for the minimum number of samples
Systems & Control Letters
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
On the optimal search problem: the case when the target distribution is unknown
SCCC '97 Proceedings of the 17th International Conference of the Chilean Computer Science Society
Gaussian Process Regression: Active Data Selection and Test Point Rejection
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
The Mathematics of Internet Congestion Control (Systems and Control: Foundations and Applications)
The Mathematics of Internet Congestion Control (Systems and Control: Foundations and Applications)
Convex Optimization
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A system performance approach to OSNR optimization in optical networks
IEEE Transactions on Communications
When do heavy-tail distributions help?
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Entropy expressions and their estimators for multivariate distributions
IEEE Transactions on Information Theory
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In many real world problems, optimisation decisions have to be made with limited information. The decision maker may have no a priori or posteriori data about the often nonconvex objective function except from on a limited number of data points. The scarcity of data may be due to high cost of observation or fast-changing nature of the underlying system. This paper presents a "black-box" optimisation framework that takes into account the information collection, estimation, and optimisation aspects in a holistic and structured manner. Explicitly quantifying the observations at each optimisation step using the entropy measure from information theory, the often nonconvex-objective function to be optimised is modelled and estimated by adopting a Bayesian approach and using Gaussian processes as a state-of-the-art regression method. The resulting iterative scheme allows the decision maker to address the problem by expressing preferences for each aspect quantitatively and concurrently.