Results 321 to 330 of about 6,227,125 (359)
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IEEE Transactions on Evolutionary Computation, 2014
Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary ...
K. Deb, Himanshu Jain
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Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary ...
K. Deb, Himanshu Jain
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American Economic Review, 2005 Forward-looking agents care about expected future utility flows, and hence have higher current felicity if they are optimistic. This paper studies utility-based biases in beliefs by supposing that beliefs maximize average felicity, optimally balancing this benefit of optimism against the costs of worse decision making.
Brunnermeier, Markus K +1 more
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Optimization by Simulated Annealing
Science, 1983There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given ...
S. Kirkpatrick +2 more
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
Journal of machine learning research, 2011We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning. Metaphorically, the adaptation allows us to find needles
John C. Duchi, Elad Hazan, Y. Singer
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Genetic Algorithms in Search Optimization and Machine Learning
, 1988From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many ...
D. Goldberg
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Optimal harvesting and optimal vaccination
Mathematical Biosciences, 2007Two optimization problems are considered: Harvesting from a structured population with maximal gain subject to the condition of non-extinction, and vaccinating a population with prescribed reduction of the reproduction number of the disease at minimal costs.
Johannes Müller, K.P. Hadeler
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Proceedings of the thirty-second annual ACM symposium on Theory of computing, 2000
We study the it static membership problem: Given a set S of at most n keys drawn from a universe U of size m, store it so that queries of the form "Is u in S?" can be answered by making few accesses to the memory. We study schemes for this problem that use space close to the information theoretic lower bound of $\Omega(n\log(\frac{m}{n}))$ bits and yet
Buhrman, Harry +3 more
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We study the it static membership problem: Given a set S of at most n keys drawn from a universe U of size m, store it so that queries of the form "Is u in S?" can be answered by making few accesses to the memory. We study schemes for this problem that use space close to the information theoretic lower bound of $\Omega(n\log(\frac{m}{n}))$ bits and yet
Buhrman, Harry +3 more
openaire +5 more sources
Random Search for Hyper-Parameter Optimization
Journal of machine learning research, 2012Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid ...
J. Bergstra, Yoshua Bengio
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NeuroImage, 2002
Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration.
M. Jenkinson +3 more
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Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration.
M. Jenkinson +3 more
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Practical Methods of Optimization
, 1988Preface Table of Notation Part 1: Unconstrained Optimization Introduction Structure of Methods Newton-like Methods Conjugate Direction Methods Restricted Step Methods Sums of Squares and Nonlinear Equations Part 2: Constrained Optimization Introduction ...
R. Fletcher
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