2 edition of **adaptive random-search algorithm for implementation of the maximum principle** found in the catalog.

adaptive random-search algorithm for implementation of the maximum principle

Elwood C. Stewart

- 231 Want to read
- 11 Currently reading

Published
**1970**
by National Aeronautics and Space Administration; [for sale by the Clearinghouse for Federal Scientific and Technical Information, Springfield, Va.] in Washington
.

Written in English

- Mathematical optimization -- Data processing.,
- Differential equations, Nonlinear -- Data processing.,
- Maximum principles (Mathematics)

**Edition Notes**

Statement | by Elwood C. Stewart, William P. Kavanaugh, and David H. Brocker. |

Series | NASA technical note, NASA TN D-5642 |

Contributions | Kavanaugh, William P., joint author., Brocker, David H., joint author. |

Classifications | |
---|---|

LC Classifications | TL521 .A3525 no. 5642, QA402.5 .A3525 no. 5642 |

The Physical Object | |

Pagination | iii, 67 p. |

Number of Pages | 67 |

ID Numbers | |

Open Library | OL5738527M |

LC Control Number | 70606135 |

Genetic algorithms simulate the process of natural selection which means those species who can adapt to changes in their environment are able to survive and reproduce and go to next generation. In simple words, they simulate “survival of the fittest” among individual of consecutive generation for solving a problem. An Efficiency-Based Adaptive Refinement Scheme Applied to Incompressible, Resistive Magnetohydrodynamics. Numerical Aspects and a Highly Parallel Implementation. Pages A Discrete Maximum Principle for Nonlinear Elliptic Systems with Interface Conditions.

Although, in principle, a variety of optimization techniques (e.g., simulated annealing 45) might have been used, there are compelling reasons why our adaptation algorithm is based on Bayesian optimization, namely because (1) it is a principled approach to optimize an unknown cost/reward function when only a few dozen of samples are possible eBook is an electronic version of a traditional print book THE can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THE is used solely as a reading device such as Nuvomedia's Rocket eBook.).

New Books for 02/25/ AUTHOR: Menezes, Flavio M. Maximum principles on Riemannian manifolds and applications / Stefano Pigola, Marco Rigoli, Alberto G. Setti. Applied pattern recognition: algorithms and implementation in C++ / Dietrich W.R. Paulus, Joachim Hornegger. EDITION: 4th ed. We also discuss different versions of the discrete maximum principle in the lowest-order Raviart–Thomas method. Finally, we recall mixed finite element methods on general polygonal meshes and show that they are a special type of the mimetic finite difference, mixed finite volume, and hybrid finite volume family.

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Chapters 4 through 9 considered the transient response characteristics and implementation considerations associated with different classes of algorithms that are widely used for adaptive array applications.

This chapter summarizes the principal characteristics of each algorithm class before considering some practical problems associated with adaptive array system : Robert A.

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In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Cited by: 3. Parallel adaptive genetic algorithm based on cloud computing Adaptive genetic algorithm.

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Steganography in Digital Media: Principles, Algorithms, and Applications [Book Reviews] Article in IEEE Signal Processing Magazine 28(5) September with. In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).

Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. A two-stage supply chain network design problem with a minimization type cost-based objective function is focused in this study.

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On the efficiency of items selection, the execution time to SA search, exhaustive search, and random search method were compared to implement the SA algorithm assessment. The experiment is performed 10 times on each of item pool of three methods. Table 2 shows the results of average execution time of selecting an item from each of the item pools.

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IHR is a random search based GO algorithm that can be used to solve both continuous and discrete optimization problems.

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