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Sakawa M. — Genetic algorithms and fuzzy multiobjective optimization
Sakawa M. — Genetic algorithms and fuzzy multiobjective optimization

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Название: Genetic algorithms and fuzzy multiobjective optimization

Автор: Sakawa M.

Аннотация:

Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a wide range of actual real world applications. The theoretical material and applications place special stress on interactive decision-making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness. The intended readers of this book are senior undergraduate students, graduate students, researchers, and practitioners in the fields of operations research, computer science, industrial engineering, management science, systems engineering, and other engineering disciplines that deal with the subjects of multiobjective programming for discrete or other hard optimization problems under fuzziness. Real world research applications are used throughout the book to illustrate the presentation. These applications are drawn from complex problems. Examples include flexible scheduling in a machine center, operation planning of district heating and cooling plants, and coal purchase planning in an actual electric power plant.


Язык: en

Рубрика: Computer science/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: 1st

Год издания: 2002

Количество страниц: 291

Добавлена в каталог: 12.03.2011

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
$\alpha$-level set      71 120 161
$\alpha$-multiobjective 0-1 programming      71
$\alpha$-multiobjective integer programming      120
$\alpha$-Pareto optimal solution      72 121 161
Active schedule      173 195
Addition      193
Aggregation function      57 73 109 122
Agreement index      191 208
Augmented minimax problem      58 74 111 123 156 163
Average agreement index      209
Backtracking      40 45
Binary string      16
Bisection method      146
Bit string      16
Boundary mutation      140
Branch and bound method      179
Chromosome      12 16
Coding      12 16
Complete optimal solution      55 108 154
Completion time      171 191
Conflict set      175 196
Convex programming      135
Convex programming problem      136
Crossover      13 230
Cut      174 195
Cycle crossover: CX      25
Decision variable      135 154 160
Decoding      12 16
Decoding algorithm      32 41 45 86 100 244
Degree of similarity      176 198
Discrete optimization problem      11
Domain constraint      135
Double string      31 85
Due date      171 224
Elitism      33 247
Elitist expected value selection      33 45 49 90 103 247
Elitist preserving selection      20 49 90 103
Equality constraint      135 154 160
Expected value selection      33 49 90 103 247
Expected-value selection      20 21
Extension Principle      193
Feasible region      135
Feasible set      135
Fitness      11 12 16 31 32 89
Fitness function      156 163
FJSP      191
Floating-point representation      137
Flow-shop scheduling problem      170
Fuzzy completion time      191
Fuzzy decision      57 109 210
Fuzzy due date      191 208
Fuzzy equal      155 162
Fuzzy goal      56 108 155 161 209
Fuzzy job-shop scheduling problem      191
Fuzzy max      155 162
Fuzzy min      155 162
Fuzzy multiobjective decision-making problem      57 109
Fuzzy number      70 120 160
Fuzzy processing time      191 208
Gannt chart      172
Gene      12 16
Generalized a-MONLP      162
Generalized multiobjective nonlinear programming problem      155
Generation      12
Genetic algorithm      11 133 170 229 237 261
GENOCOP      142
GENOCOP III      142
Genotype      12 16
Giffler and Thompson algorithm      174 195
Giffler and Thompson algorithm-based crossover      177 198
Greatest associate ordinary number      210
Heuristic crossover      139
Individual      11
Inequality constraint      135 154 160
Initial population      88 89 137 176 197 212 230 262
Initial reference point      143
inversion      26 35 94 248
Job-shop scheduling problem      169 171 191
JSP      169 171 191
Knapsack problem      30 84
Linear membership function      56 108 209
Linear scaling      17 32 90 246
Local Pareto optimal solution      155
Locus      12 16
M-a-Pareto optimal solution      162
M-Pareto optimal solution      156
Machining center      224
Makespan      171
Maximum completion time      171
Maximum fuzzy completion time      209
Membership function      155 160 209
Minimax problem      58 74 110 123 156 163
Minimum agreement index      209
Minimum operator      57 109 210
MO0-1-FN      70
MOFJSP      209
MOIP-FN      118
MONLP      154 159
MONLP-FN      160
Monthly processing plan      224
Multiobjective 0-1 programming      54
Multiobjective fuzzy job-shop scheduling problem      209
Multiobjective integer programming      108
Multiobjective multidimensional 0-1 knapsack problem      55
Multiobjective multidimensional integer knapsack problem      108
Multiobjective nonlinear programming      154 159
Multipoint crossover      23
Mutation      13 26 35 93 179 200 213 231 248 262
Natural selection      19
Nondelay schedule      173
Noninferior solution      55
Nonlinear programming      135
Nonuniform mutation      140
Objective function      135 154 160
One-point crossover      23 137 248
Optimal schedule      174
Ordered crossover: OX      25
Pareto optimal solution      55 154
Pareto optimality      55
Pareto optimality test      58 111 156
Partially matched crossover: PMX      25
Phenotype      12 16
PMX for double strings      33 91
Population      11 12 15
Power law scaling      19
Ranking method      21 210
Ranking selection      20 21 230 262
Reference membership levels      58 110 156 163
Reference membership values      74 122
Reference point      58 74 110 122
Reference points      142
Reference solution      40 45 99 243
Reproduction      13 19 33 90
Revised GENOCOP III      143
Roulette selection      19
Satisficing solution      2 59 111 157 164
Scheduling problem      227
Search points      142
Selection      19
Semiactive schedule      173
Sigma scaling      19
Sigma truncation      19
Simple crossover      24 137
Simulated annealing      182 200 213 265
Single arithmetic crossover      138
String      12 16
Triangular fuzzy number      191
Two-point crossover      24 262
Unconstrained minimization problem      135
Uniform crossover      23
Uniform mutation      140
V (max) operation      193
Weak Pareto optimal solution      56
Weak Pareto optimality      55
Whole arithmetic crossover      138
Whole nonuniform mutation      141
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