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Burke E.K., Kendall G. — Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques
Burke E.K., Kendall G. — Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques



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Название: Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques

Авторы: Burke E.K., Kendall G.

Аннотация:

Search Methodologies is a tutorial survey of the methodologies that are at the confluence of several fields: Computer Science, Mathematics and Operations Research. It is a carefully structured and integrated treatment of the major technologies in optimization and search methodology. The book is made up of 19 chapters. The chapter authors are drawn from across Computer Science and Operations Research and include some of the world's leading authorities in their field.

The result is a major state-of-the-art tutorial text of the main optimization and search methodologies available to researchers, students and practitioners across discipline domains in applied science. It can be used as a textbook or a reference book to learn and apply these methodologies to a wide range of today's problems. It has been written by some of the world's most well known authors in the field.


Язык: en

Рубрика: Computer science/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2005

Количество страниц: 620

Добавлена в каталог: 13.05.2008

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
University exam timetabling      see “Timetabling”
Unsupervised learning      342 357 368
Upper approximations      476—504 511 520
Upper bound      27 29 30 35—37 45 47 49 51 56 58 77 246 266 281 291 418 423 448 580
Utility service optimization      240
Utopian objective vector      281
Variable, consistency model      498
Variable, generation      87—89
Variable, neighborhood search      211—238
Variable, neighborhood search, reduced      211 219 220 222 233
Variable, neighborhood search, skewed      211—226
Variable, neighborhood search, variable neighborhood decomposition search      211
Variable, neighborhood search, variable neighborhood descent      211
Variable, neighborhood search, VNS within exact algorithm      231
Variable, precision rough set approach      498
Vehicle routing      178 544 553
Verifiable      563
Visual CHIP      258
VNDS      211 213 227 228 230
VNS      see “Variable neighborhood search”
Vogel’s approximation method      57
Waiting-time model      602
Weak preference relation      493 508
Weighted maximum satisfiability      226
Weighted-sum approach      289—291
XCS      534—536
Y-reduct      486
Zero-argument functions      129
Zykov’s algorithm      33
“Axes” of a search space      587
1 2 3 4
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