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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
Exchangeable, attributes      481
Exchangeable, criteria      499 504
Exhaustive search      10—11
Expanded formulation      80
Facility location      70 78 80 83 91
Fail first      245
Fast Light Toolkit      547
Feasible solution      8
Financial decision support system      241
Finite-state machines      358
FireWall      380
First descent heuristic      215
First improvement strategy      590
First-fit-decreasing algorithm      533
Fish schooling      401 416
Fitness      9
Fitness, endogenous function      113
Fitness, exogenous function      113
Fitness, inheritance      113
Fitness, landscapes      587—610
Fitness, landscapes, an example      589
Fitness, landscapes, empirical studies      603
Fitness, landscapes, mathematical characterization      593
Fitness, landscapes, practical applications      603
Fitness, measure      98 129 131 134 139
Fitness, of a neural network      361
Fitness, proportionate selection      99 142
Fixed charge      77 170
Flow augmenting chains      46 47 50 57
Floyd’s shortest path algorithm      55
Ford — Fulkerson algorithm      46 47 53
Forward recursion      38
FPTAS      see “Fully polynomial time approximation scheme”
Frequency assignment problem      546
Full initialization method      133
Fully polynomial time approximation scheme      559 574
Fuzzy, adaptive control schemes      461—463
Fuzzy, CSP      250
Fuzzy, implication      447
Fuzzy, inference system      449—454
Fuzzy, inference system, defuzzification unit      450
Fuzzy, inference system, fuzzification unit      449
Fuzzy, inference system, fuzzy logic reasoning unit      449
Fuzzy, inference system, knowledge base      449
Fuzzy, inference system, max-min fuzzy inference method      451
Fuzzy, inference system, max-product fuzzy inference method      451
Fuzzy, logic      369 439 442
Fuzzy, measures      500
Fuzzy, reasoning      437—474
Fuzzy, relation      446
Fuzzy, set composition      447
Fuzzy, set operations      443
Fuzzy, sets      437—467
Fuzzy, similarity measures      448
Fuzzy, similarity measures, L-fuzzy similarity      449
Fuzzy, similarity measures, M-fuzzy similarity      449
Fuzzy, similarity measures, P-fuzzy similarity      449
Fuzzy, similarity measures, S-fuzzy similarity      449
Fuzzy, similarity measures, W-fuzzy similarity      449
Fuzzy, systems, modeling      463
Fuzzy, systems, stability      464
GA      see “Genetic algorithm”
Gene, deletion      127
Gene, duplication      127
Generalization test      344 357
Generation probability      193 195
Genes      97 102 103 156 549 550 603
Genetic algorithm      97—125 127 168 178 247 308 357 358 381 383 385 386 389 395 421 426 535 537 545 547 549 550 552 589 590
Genetic programming      127—164
Genotype      306 361 362 386
Global constraints      254—258
Global optimum      10
Gomory — Chvatal procedure      81
GP      see “Genetic programming”
Gradient descent algorithm      355
Granulation      476
Granules of knowledge      479
Graph, algorithm      56
Graph, bipartite      58
Graph, coloring      30—35 84—89 204 224 239 558
Graph, complete      33—35
Graph, complete bipartite      565
Graph, eigensystem      597—599
Graph, partitioning      188 205 603
Graph, problem      45
Graph, representation      595—596
Graph, theory      19 30 62 178 231 321 437 605
Gray code      591
Greedy, approximation algorithms      563
Greedy, heuristic      218 224
Greedy, knapsack      568
Greedy, MAX-CUT      566
Greedy, MAX-SAT      565
Greedy, vertex cover      564
Grow initialization method      134
H-means      218
HAL      258
Hamiltonian path problem      563
Hamming, distance      217 226 231 382 594
Hamming, landscape      594 598
Hard constraints      see “Constraint hard”
Headless chicken crossover      137
Heaviside function      353
Hebbian learning rule      354
Hedge      see “Transformation operator”
Heuristics      11—12
Heuristics to choose heuristics      see “Hyper-heuristics”
Hill climbing      12—13
Homogeneous      45 54 194 202 206
Hopfield networks      356
Human-competitive      147—149 156
Hyper-heuristics      14 529—556
Hyper-planes      20
Idiotypic networks      386—390
Inclusion property      484 496
Incomplete search technique      269
Independent variables      129
Indicator function      594
Indirect encoding of neural networks      362
Indisceraibility relation      483 493
Indiscernibility      476 477 488
Indispensable      438
Indispensable, attribute      480 482 485
Indispensable, criteria      499 504
Inductive learning      344—348 487
Inductive logic programming      342 346—348
Inertia weight      418
Infeasible solutions      8
Inference methods      242 243
Inference rules      439 448
Infix-notation      128
Information, gain      345
Information, theory      341 345
Information, transfer      408
Inhomogeneous algorithm      195 206
Initial random population      136 138 142 144
Initial solution      12
Initialization      98 133 134 156 229 360 383 425 550
Innovation      99 106 302
Integer programming      69—95
Integer quantities      76
Intensification      175 179 180 234 404 413
Interchangeability      243
Interior point      24 90
Intermediate vectors      604
Intermediate-term memory      175
Interpolation      343
Intersection $\cap$      252 439 444 476 479 480 486 496 498 510 512
Intractable      110 248 557
intrusion detection systems      see “Artificial immune systems”
Invariant      109 231 257
Inverse consistency      243
Inverse problem      448
Irreducible      194
Iterative improvement      188 189 192 205
Job shop scheduling      165 169—171 175 188 207 395 410 537 545 571
K-means      219
Kilter, diagram      49
Kilter, line      49 57
Knapsack, binary knapsack problem      58
Knapsack, bounded knapsack problem      58
Knapsack, maximum fraction      568
Knapsack, maximum integer      568
Knapsack, problem      30 42—44 63 86 561 568 570 604
Knapsack, unbounded knapsack problem      38 58
Kruskal’s greedy algorithm      540
KUR problem      296
Lagrangian relaxation      36
Laplacian matrix      597
Learning      344—346
Learning, algorithms      333
Learning, Bayesian      351
Learning, bottom-up      246 347
Learning, chess play learning      343
Learning, classifier systems      357 358 396
Learning, decision-tree      347
Learning, element      341
Learning, inductive      348
Learning, reinforcement      351
Learning, robot learning      343
Learning, sample      490
Learning, sequence of actions      343
Learning, top-down      347
Level of confidence      487
Limited discrepancy search      246
Linear programming      20—24
Linear propagation      256
Linear relaxation      72 79—93
Linearly separable      354
Lisp S-expressions      128
Local optimum      10
Local search      12—13 112 115
Logic programming      258
Logical constraints      78
Long-term memory      175
Look ahead      246
Lower approximations      476 477 480 512 516
Lower bound      27 28 30 32 35 36 44 48—50 53 57 58
Machine learning      341—373
Makespan      166 537 604
Management science      8
Markov chains      193 195 202 203 206
Matching function      381 384 388
Matching problems      58
Mating pool      99 100
MAX-Ak-SAT      573
Max-closure      250
Max-CSP problem      250
Max-cut problem      567
MAX-SAT      565 573 575
Maximum flow problem      45—48
Maximum weighted independent set      89
McCulloch — Pitts neurons      353
Mean square error      354 361
Membership function      439 440 446 450 452 458 462 465 485
Membership function, Gaussian      442
Membership function, monotonically decreasing linear      441
Membership function, monotonically decreasing sigmoidal      442
Membership function, monotonically increasing linear      441
Membership function, monotonically increasing sigmoidal      441
Membership function, n-membership function      442
Membership function, trapezoid      440
Membership function, triangular      440
Memetic algorithm      112 115 116 395
Messy evolutionary search      551 552
Metaheuristics      13—14
Metropolis algorithm      190 191 608
Min-conflicts heuristic      247
Minimal domain size      245
Minimum, cost flow problem      48—53
Minimum, job scheduling      571
Minimum, spanning trees      541
Minimum, vertex cover      561 564 565
Mixed integer programming      178
Modern heuristics      see “Metaheuristics”
Modifier      see “Transformation operator”
MOEA      see “Evolutionary algorithm multiobjective”
Monte Carlo      190 233 351
MOOP      see “Multi-objective optimization problem”
Multi-attribute      475 519 521
Multi-modal optimization      274 275
Multi-objective optimization      273—316
Multicriteria      475 476 491 503—507 518 519 521
Multigraded dominance      510—512 516
Multilayer feedforward neural networks      355
Multimodal landscapes      595
Multiple function sets      144
Multiple terminal sets      144
Multistage programming      37
Mutation      61 98 99 104 113 127 132 133 137—139 144 151 157 304 358 359 361 363 379 381 385
Mutation, probability      105 115
Nadir objective vector      281
Negative dominance cones      494
Negative selection      378 383—385 393
Neighbor      188 589
Neighborhood      188 589
Neighborhood, graph      189
Neighborhood, relations      425
Neighborhood, search      211
Neighborhood, search, variable      238
Neighborhood, structure      169—171 589 593—594
Network flow programming      20
Network, flow      20 24 53 57 60 261
Network, flow programming      19 45—54 62 64
Network, management      240
Network, simplex algorithm      53
Neural networks      353—357 360—365
Niching operator      305
No free lunch      317—339
Node potentials      49
Non-dominated set      285 304
Non-dominated solutions      280—289 292 296 304 306
Non-dominated sorting algorithm      288 293 295 299
Non-evolutionary multi-objective optimization      308
Non-linear programming problems      273
Non-parametric estimates      602
Nondeterministic polynomial      318 562
Nonself cells      375
NP-complete      54 319
NP-hard      167 187 248 319 557
NPO      563
Nurse rostering      9
Objective function      9
Objective space      275 276 306 308
Off-policy      351
On-line      134 352 462 570
On-policy      351 352
Open-shop      537
Operations research      7—8
Operations Research library      545
OPL      92 258
Optimal solutions      10
Optimality      10 273
Optimization      54 87 90 97 115 201 261 273 306 308 357 386 395 415 416 425 428 565 598
Optimization, algorithm      110 187 279 282 303 329 333
Order      see “O notation”
Ordinal selection      100
Out of kilter      49—53 56 60
Outranking      477 493 508 511 516
P      317
1 2 3 4
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