Stochastic Optimization
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Home > Art, Film & Photography > Stochastic Optimization: Ant Colony Optimization Algorithms, Brst Algorithm, CMA-Es, Multi-Armed Bandit, Natural Evolution Strategy, Quantum an
Stochastic Optimization: Ant Colony Optimization Algorithms, Brst Algorithm, CMA-Es, Multi-Armed Bandit, Natural Evolution Strategy, Quantum an

Stochastic Optimization: Ant Colony Optimization Algorithms, Brst Algorithm, CMA-Es, Multi-Armed Bandit, Natural Evolution Strategy, Quantum an


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About the Book

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 28. Chapters: Ant colony optimization algorithms, BRST algorithm, CMA-ES, Multi-armed bandit, Natural evolution strategy, Quantum annealing, Random search, Scenario optimization, Simulation Optimization Library: Throughput Maximization, Stochastic approximation, Stochastic gradient descent, Stochastic programming, Stochastic tunneling. Excerpt: CMA-ES stands for Covariance Matrix Adaptation Evolution Strategy. Evolution strategies (ES) are stochastic, derivative-free methods for numerical optimization of non-linear or non-convex continuous optimization problems. They belong to the class of evolutionary algorithms and evolutionary computation. An evolutionary algorithm is broadly based on the principle of biological evolution, namely the repeated interplay of variation (via mutation and recombination) and selection: in each generation (iteration) new individuals (candidate solutions, denoted as ) are generated by variation, usually in a stochastic way, and then some individuals are selected for the next generation based on their fitness or objective function value . Like this, over the generation sequence, individuals with better and better -values are generated. In an evolution strategy, new candidate solutions are sampled according to a multivariate normal distribution in the . Pairwise dependencies between the variables in this distribution are represented by a covariance matrix. The covariance matrix adaptation (CMA) is a method to update the covariance matrix of this distribution. This is particularly useful, if the function is ill-conditioned. Adaptation of the covariance matrix amounts to learning a second order model of the underlying objective function similar to the approximation of the inverse Hessian matrix in the Quasi-Newton method in classical optimization. In contrast to most classical methods, fewer assumptions...


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Product Details
  • ISBN-13: 9781230776125
  • Publisher: Booksllc.Net
  • Publisher Imprint: Booksllc.Net
  • Height: 246 mm
  • No of Pages: 30
  • Spine Width: 2 mm
  • Weight: 73 gr
  • ISBN-10: 1230776125
  • Publisher Date: 13 Sep 2013
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: Ant Colony Optimization Algorithms, Brst Algorithm, CMA-Es, Multi-Armed Bandit, Natural Evolution Strategy, Quantum an
  • Width: 189 mm


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