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Pascal Programs Printed in GENETIC ALGORITHMS IN SEARCH, OPTIMIZATION, AND MACHINE LEARNING
by David E. Goldberg
標(biāo)簽：
OPTIMIZATION
ALGORITHMS
LEARNING
Programs
上傳時(shí)間：
2015-04-19
上傳用戶：
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GloptiPoly 3: moments, optimization and
semidefinite programming.
Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem of
Moments (GPM), an infinite-dimensional optimization problem which can be viewed as
an extension of the classical problem of moments [8]. From a theoretical viewpoint, the
GPM has developments and impact in various areas of mathematics such as algebra,
Fourier analysis, functional analysis, operator theory, probability and statistics, to cite
a few. In addition, and despite a rather simple and short formulation, the GPM has a
large number of important applications in various fields such as optimization, probability,
finance, control, signal processing, chemistry, cristallography, tomography, etc. For an
account of various methodologies as well as some of potential applications, the interested
reader is referred to [1, 2] and the nice collection of papers [5].
標(biāo)簽：
optimization
semidefinite
programming
GloptiPoly
上傳時(shí)間：
2016-06-05
上傳用戶：lgnf
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Computational models are commonly used in engineering design and scientific discovery activities for simulating
complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear
structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who
want to develop an understanding and/or predictive capability for complex behaviors typically observed in the
corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system
parameters, such as size or location dimensions and material properties, are adjusted to improve the performance
of a system, as defined by one or more system performance objectives. Such optimization or tuning of the
virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system
parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for
example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or
to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often
like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These
approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling
methods are often used in uncertainty quantification to calculate a distribution on system performance measures,
and to understand which uncertain inputs contribute most to the variance of the outputs.
A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic
and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased
models. These capabilities generally lead to improved designs and system performance in earlier design
stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product
development costs. In addition to providing this practical environment for answering system performance questions,
the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized
methods and meta-algorithms
標(biāo)簽：
Optimization and Uncertainty Quantification
上傳時(shí)間：
2016-04-08
上傳用戶：huhu123456
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Simple GA code (Pascal code from Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning.)
標(biāo)簽：
D.
E.
code
Optimization
上傳時(shí)間：
2014-12-07
上傳用戶：wlcaption
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Each of us is interested in optimization, and telecommunications. Via several meetings,
conferences, chats, and other opportunities, we have discovered these joint interests and
decided to put together this book.
標(biāo)簽：
Telecommunications
optimization
heuristics
上傳時(shí)間：
2020-06-01
上傳用戶：shancjb
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包括了汽車(chē)安全系統(tǒng)、多媒體和汽車(chē)網(wǎng)絡(luò)；未來(lái)汽車(chē)信息終端平臺(tái)研制，智能無(wú)線通訊在促進(jìn)汽車(chē)安全應(yīng)用中的作用，汽車(chē)電子檢測(cè)平臺(tái)，以及風(fēng)河公司的Device Software Optimization and Wind River Automotive等，高端研討，值得一看。
標(biāo)簽：
汽車(chē)安全系統(tǒng)
多媒體
信息終端
汽車(chē)
上傳時(shí)間：
2014-01-06
上傳用戶：jkhjkh1982
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Beginning with an overview of SQL Server 2000, this book discusses online transaction processing (OLTP) and online analytical processing (OLAP), features a tour of different SQL Server releases, and offers a guide to installation. The author describes and demonstrates the changes since SQL Server 7.0, thoroughly exploring SQL Server 2000 s capacity as a Web-enabled database server. Readers are then immersed in advanced database administration topics such as performance optimization and debugging techniques.
標(biāo)簽：
transaction
processing
Beginning
discusses
上傳時(shí)間：
2013-11-28
上傳用戶：eclipse
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經(jīng)典英文原版PHP教程networking, data structures, regular expressions, math, configuration, graphics, MySQL/PostgreSQL support, XML, algorithms, debugging, optimization...and 650 downloadable code examples, with a Foreword by PHP 5 contributor and Zend Engine 2 co-creator Andi Gutmans!
標(biāo)簽：
configuration
expressions
networking
structures
上傳時(shí)間：
2014-01-28
上傳用戶：cuibaigao
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genetic algorithm (or GA) is a search technique used in computing to find true or approximate solutions to optimization and search problems
for function of 2 variable
標(biāo)簽：
approximate
algorithm
computing
technique
上傳時(shí)間：
2017-07-25
上傳用戶：225588
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Evolutionary Computation (EC) deals with problem solving, optimization, and
machine learning techniques inspired by principles of natural evolution and ge-
netics. Just from this basic definition, it is clear that one of the main features of
the research community involved in the study of its theory and in its applications
is multidisciplinarity. For this reason, EC has been able to draw the attention of
an ever-increasing number of researchers and practitioners in several fields.
標(biāo)簽：
Applications
Evolutionary
Computing
of
上傳時(shí)間：
2020-05-26
上傳用戶：shancjb
