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Noisy Derivative-Free Optimization With Value Suppression
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Derivative-free optimization has shown advantage in solving sophisticated problems such as policy search, when the environment is noise-free. Many real-world environments are noisy, where solution evaluations are inaccurate due to the noise.
Hong Wang, Hong Qian, Yang Yu 0001
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Derivative-Free Optimization via Classification
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 Many randomized heuristic derivative-free optimization methods share a framework that iteratively learns a model for promising search areas and samples solutions from the model. This paper studies a particular setting of such framework, where the model is implemented by a classification model discriminating good solutions from bad ones.
Yang Yu 0001, Hong Qian, Yi-Qi Hu
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Derivative free optimization methods for optimizing stirrer configurations
European Journal of Operational Research, 2008 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ömür Ugur +3 more
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Comparative Study of Derivative Free Optimization Algorithms
IEEE Transactions on Industrial Informatics, 2011Derivative free optimization algorithms are often used when it is difficult to find function derivatives, or if finding such derivatives are time consuming. The Nelder Mead's simplex method is one of the most popular derivative free optimization algorithms in the fields of engineering, statistics, and sciences. This algorithm is favored and widely used
A Malinowski
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Decomposition in derivative-free optimization
Journal of Global Optimization, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kaiwen Ma +4 more
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Derivative-Free and Blackbox Optimization [PDF]
, 2017 This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization.
Charles Audet, Warren Hare
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Efficient derivative-free optimization
2007 46th IEEE Conference on Decision and Control, 2007The present paper considers the derivative-free optimization of expensive non-smooth functions. One of the most efficient algorithms for this class of problems is the surrogate-based optimization framework by Booker et al, 1999. Searches performed using this algorithm are restricted to points lying on an underlying grid to keep function evaluations far
Paul Belitz, Thomas Bewley
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Openly revisiting derivative-free optimization
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019This paper surveys and compares a wide range of derivative-free optimization algorithms in an open source context. We also propose a genetic variant of differential evolution, an adaptation of population control for the multimodal noise-free case, new multiscale deceptive functions, and as a contribution to the debate on genetic crossovers, a test ...
Jérémy Rapin +6 more
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2011
In many engineering applications it is common to find optimization problems where the cost function and/or constraints require complex simulations. Though it is often, but not always, theoretically possible in these cases to extract derivative information efficiently, the associated implementation procedures are typically non-trivial and time-consuming
Oliver Kramer +2 more
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In many engineering applications it is common to find optimization problems where the cost function and/or constraints require complex simulations. Though it is often, but not always, theoretically possible in these cases to extract derivative information efficiently, the associated implementation procedures are typically non-trivial and time-consuming
Oliver Kramer +2 more
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Derivative Free Optimization in Higher Dimension
International Transactions in Operational Research, 2001Non‐linear optimizations that do not require explicit or implicit derivative information of an objective function are an alternate search strategy when the derivative of the objective function is not available. In factorial design, the number of trials for experimental identification method in Em is about (m+ 1).
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