Introduction to genetic algorithms:
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| Language: | English |
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Berlin [u.a.]
Springer
2008
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| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIX, 442 S. graph. Darst. |
| ISBN: | 9783540731894 354073189X |
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| 020 | |a 354073189X |c Gb. : ca. EUR 74.85 (freier Pr.), ca. sfr 115.00 (freier Pr.) |9 3-540-73189-X | ||
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| 100 | 1 | |a Sivanandam, S. N. |e Verfasser |0 (DE-588)132409747 |4 aut | |
| 245 | 1 | 0 | |a Introduction to genetic algorithms |c S. N. Sivanandam ; S. N. Deepa |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2008 | |
| 300 | |a XIX, 442 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Algorithmes génétiques | |
| 650 | 4 | |a Programmation génétique (Informatique) | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 4 | |a Genetic programming (Computer science) | |
| 650 | 0 | 7 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Deepa, S. N. |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016710993&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016710993 | ||
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| _version_ | 1804137977447710721 |
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| adam_text | Contents
1
Evolutionary Computation
....................................... 1
1.1
Introduction
.................................................. 1
1.2
The Historical Development of EC
............................... 2
1.2.1
Genetic Algorithms
...................................... 2
1.2.2
Genetic Programming
.................................... 3
1.2.3
Evolutionary Strategies
.................................. 4
1.2.4
Evolutionary Programming
............................... 5
1.3
Features of Evolutionary Computation
............................ 5
1.3.1
Particulate Genes and Population Genetics
.................. 6
1.3.2
The Adaptive Code Book
................................. 7
1.3.3
The Genotype/Phenotype Dichotomy
....................... 8
1.4
Advantages of Evolutionary Computation
......................... 9
1.4.1
Conceptual Simplicity
................................... 10
1.4.2
Broad Applicability
..................................... 10
1.4.3
Hybridization with Other Methods
......................... 11
1.4.4
Parallelism
............................................. 11
1.4.5
Robust to Dynamic Changes
.............................. 11
1.4.6
Solves Problems that have no Solutions
..................... 12
1.5
Applications of Evolutionary Computation
......................... 12
1.6
Summary
..................................................... 13
2
Genetic Algorithms
.............................................. 15
2.1
Introduction
.................................................. 15
2.2
Biological Background
......................................... 16
2.2.1
The Cell
............................................... 16
2.2.2
Chromosomes
.......................................... 16
2.2.3
Genetics
............................................... 17
2.2.4
Reproduction
........................................... 17
2.2.5
Natural Selection
........................................ 19
2.3
What is Genetic Algorithm?
..................................... 20
2.3.1
Search Space
........................................... 20
2.3.2
Genetic Algorithms World
................................ 20
2.3.3
Evolution and Optimization
............................... 22
2.3.4
Evolution and Genetic Algorithms
......................... 23
2.4
Conventional Optimization and Search Techniques
.................. 24
2.4.1
Gradient-Based Local Optimization Method
................. 25
2.4.2
Random Search
......................................... 26
2.4.3
Stochastic Hill Climbing
................................. 27
2.4.4
Simulated Annealing
.................................... 27
2.4.5
Symbolic Artificial Intelligence
(AI)
....................... 29
2.5
A Simple Genetic Algorithm
.................................... 29
2.6
Comparison of Genetic Algorithm with Other
Optimization Techniques
....................................... 33
2.7
Advantages and Limitations of Genetic Algorithm
.................. 34
2.8
Applications of Genetic Algorithm
............................... 35
2.9
Summary
..................................................... 36
3
Terminologies and Operators of GA
............................... 39
3.1
Introduction
.................................................. 39
3.2
Key Elements
................................................. 39
3.3
Individuals
................................................... 39
3.4
Genes
....................................................... 40
3.5
Fitness
....................................................... 41
3.6
Populations
................................................... 41
3.7
Data Structures
................................................ 42
3.8
Search Strategies
.............................................. 43
3.9
Encoding
..................................................... 43
3.9.1
Binary Encoding
........................................ 43
3.9.2
Octal Encoding
......................................... 44
3.9.3
Hexadecimal Encoding
................................... 44
3.9.4
Permutation Encoding (Real Number Coding)
............... 44
3.9.5
Value Encoding
......................................... 45
3.9.6
Tree Encoding
.......................................... 45
3.10
Breeding
..................................................... 46
3.10.1
Selection
.............................................. 46
3.10.2
Crossover (Recombination)
............................... 50
3.10.3
Mutation
............................................... 56
3.10.4
Replacement
........................................... 57
3.11
Search Termination (Convergence Criteria)
........................ 59
3.11.1
Best Individual
......................................... 59
3.11.2
Worst individual
........................................ 60
3.11.3
Sum of Fitness
.......................................... 60
3.11.4
Median Fitness
......................................... 60
3.12
Why do Genetic Algorithms Work?
............................... 60
3.12.1
Building Block Hypothesis
............................... 61
3.12.2
A Macro-Mutation Hypothesis
............................ 62
3.12.3
An Adaptive Mutation Hypothesis
......................... 62
3.12.4
The Schema Theorem
.................................... 63
3.12.5
Optimal Allocation of Trials
.............................. 65
3.12.6
Implicit
Parallelism
...................................... 66
3.12.7
The No Free Lunch Theorem
.............................. 68
3.13
Solution Evaluation
............................................ 68
3.14
Search Refinement
............................................. 69
3.15
Constraints
................................................... 69
3.16
Fitness Scaling
................................................ 70
3.16.1
Linear Scaling
.......................................... 70
3.16.2
Sigma Truncation
....................................... 71
3.16.3
Power Law Scaling
...................................... 72
3.17
Example Problems
............................................. 72
3.17.1
Maximizing a Function
.................................. 72
3.17.2
Traveling Salesman Problem
.............................. 76
3.18
Summary
..................................................... 78
Exercise Problems
............................................. 81
4
Advanced Operators and Techniques in Genetic Algorithm
.......... 83
4.1
Introduction
.................................................. 83
4.2
Diploidy, Dominance and Abeyance
.............................. 83
4.3
Multiploid
.................................................... 85
4.4
Inversion and Reordering
....................................... 86
4.4.1
Partially Matched Crossover (PMX)
........................ 88
4.4.2
Order Crossover (OX)
................................... 88
4.4.3
Cycle Crossover (CX)
................................... 89
4.5
Niche and Speciation
........................................... 89
4.5.1
Niche and Speciation in
Multimodal
Problems
............... 90
4.5.2
Niche and Speciation in Unimodal Problems
................. 93
4.5.3
Restricted Mating
....................................... 96
4.6
Few Micro-operators
........................................... 97
4.6.1
Segregation and
Translocation
............................. 97
4.6.2
Duplication and Deletion
................................. 97
4.6.3
Sexual Determination
.................................... 98
4.7
Non-binary Representation
...................................... 98
4.8
Multi-Objective Optimization
.................................... 99
4.9
Combinatorial Optimizations
....................................100
4.10
Knowledge Based Techniques
...................................100
4.11
Summary
.....................................................102
Exercise Problems
.............................................103
5
Classification of Genetic Algorithm
................................105
5.1
Introduction
..................................................105
5.2
Simple Genetic Algorithm (SGA)
................................105
5.3
Parallel and Distributed Genetic Algorithm (PGA and DGA)
.........106
5.3.1
Master-Slave Parallelization
..............................109
5.3.2
Fine Grained Parallel GAs (Cellular GAs)
...................110
5.3.3
Multiple-Deme Parallel GAs (Distributed GAs or Coarse
Grained GAs)
..........................................
Ill
5.3.4
Hierarchical Parallel Algorithms
...........................113
5.4
Hybrid Genetic Algorithm (HGA)
................................115
5.4.1
Crossover
..............................................116
5.4.2
Initialization Heuristics
..................................117
5.4.3
The RemoveSharp Algorithm
.............................117
5.4.4
The LocalOpt Algorithm
.................................119
5.5
Adaptive Genetic Algorithm
(AGA)
..............................119
5.5.1
Initialization
............................................120
5.5.2
Evaluation Function
.....................................120
5.5.3
Selection operator
.......................................121
5.5.4
Crossover operator
......................................121
5.5.5
Mutation operator
.......................................122
5.6
Fast Messy Genetic Algorithm (FmGA)
...........................122
5.6.1
Competitive Template (CT) Generation
.....................123
5.7
Independent Sampling Genetic Algorithm (ISGA)
..................124
5.7.1
Independent Sampling Phase
..............................125
5.7.2
Breeding Phase
.........................................126
5.8
Summary
.....................................................127
Exercise Problems
.............................................129
6
Genetic Programming
...........................................131
6.1
Introduction
..................................................131
6.2
Comparison of GP with Other Approaches
.........................131
6.3
Primitives of Genetic Programming
...............................135
6.3.1
Genetic Operators
.......................................136
6.3.2
Generational Genetic Programming
........................136
6.3.3
Tree Based Genetic Programming
..........................136
6.3.4
Representation of Genetic Programming
....................137
6.4
Attributes in Genetic Programming
...............................141
6.5
Steps of Genetic Programming
...................................143
6.5.1
Preparatory Steps of Genetic Programming
..................143
6.5.2
Executional Steps of Genetic Programming
..................146
6.6
Characteristics of Genetic Programming
...........................149
6.6.1
What We Mean by Human-Competitive
...................149
6.6.2
What We Mean by High-Return
.........................152
6.6.3
What We Mean by Routine
.............................154
6.6.4
What We Mean by Machine Intelligence
..................154
6.7
Applications of Genetic Programming
............................156
6.7.1
Applications of Genetic Programming
in Civil Engineering
.....................................156
6.8
Haploid Genetic Programming with Dominance
....................159
6.8.1
Single-Node Dominance Crossover
........................161
6.8.2
Sub-Tree Dominance Crossover
...........................161
6.9
Summary
.....................................................161
Exercise Problems
.............................................163
7
Genetic
Algorithm Optimization Problems
.........................165
7.1
Introduction
..................................................165
7.2
Fuzzy Optimization Problems
...................................165
7.2.1
Fuzzy Multiobjective Optimization
.........................166
7.2.2
Interactive Fuzzy Optimization Method
.....................168
7.2.3
Genetic Fuzzy Systems
..................................168
7.3
Multiobjective Reliability Design Problem
.........................170
7.3.1
Network Reliability Design
...............................170
7.3.2
Bicriteria Reliability Design
..............................174
7.4
Combinatorial Optimization Problem
.............................176
7.4.1
Linear Integer Model
....................................178
7.4.2
Applications of Combinatorial Optimization
.................179
7.4.3
Methods
...............................................182
7.5
Scheduling Problems
...........................................187
7.5.1
Genetic Algorithm for Job Shop Scheduling Problems (JSSP)
.. 187
7.6
Transportation Problems
........................................190
7.6.1
Genetic Algorithm in Solving Transportation
Location-Allocation Problems with Euclidean Distances
.......191
7.6.2
Real-Coded Genetic Algorithm (RCGA) for Integer Linear
Programming in Production-Transportation Problems
with Flexible Transportation Cost
..........................194
7.7
Network Design and Routing Problems
...........................199
7.7.1
Planning of Passive Optical Networks
......................199
7.7.2
Planning of Packet Switched Networks
.....................202
7.7.3
Optimal Topological Design of All Terminal Networks
........203
7.8
Summary
.....................................................208
Exercise Problems
.............................................209
8
Genetic Algorithm Implementation Using
Matlab
...................211
8.1
Introduction
..................................................211
8.2
Data Structures
................................................211
8.2.1
Chromosomes
..........................................212
8.2.2
Phenotypes
.............................................212
8.2.3
Objective Function Values
................................213
8.2.4
Fitness Values
..........................................213
8.2.5
Multiple
Subpopulations
.................................213
8.3
Toolbox Functions
.............................................214
8.4
Genetic Algorithm Graphical User Interface Toolbox
................219
8.5
Solved Problems using
MATLAB
................................224
8.6
Summary
.....................................................260
Review Questions
.............................................261
Exercise Problems
.............................................261
9
Genetic Algorithm Optimization in
C/C++.........................263
9.1
Introduction
..................................................263
9.2
Traveling Salesman Problem (TSP)
...............................263
9.3
Word Matching Problem
........................................271
9.4
Prisoner s Dilemma
............................................280
9.5
Maximize f(x)
=
x2
............................................286
9.6
Minimization a Sine Function with Constraints
.....................292
9.6.1
Problem Description
.....................................293
9.7
Maximizing the Function f(x)
=
x*sin(l(№*x)
+ 10...............302
9.8
Quadratic Equation Solving
.....................................310
9.9
Summary
.....................................................315
9.9.1
Projects
................................................315
10
Applications of Genetic Algorithms
..............................317
10.1
Introduction
..................................................317
10.2
Mechanical Sector
.............................................317
10.2.1
Optimizing Cyclic-Steam Oil Production
with Genetic Algorithms
.................................317
10.2.2
Genetic Programming and Genetic Algorithms
for Auto-tuning Mobile Robot Motion Control
...............320
10.3
Electrical Engineering
..........................................324
10.3.1
Genetic Algorithms in Network Synthesis
...................324
10.3.2
Genetic Algorithm Tools for Control Systems Engineering
.....328
10.3.3
Genetic Algorithm Based Fuzzy Controller for Speed Control
of Brushless DC Motor
...................................334
10.4
Machine Learning
.............................................341
10.4.1
Feature Selection in Machine learning using GA
.............341
10.5
Civil Engineering
..............................................345
10.5.1
Genetic Algorithm as Automatic Structural Design Tool
.......345
10.5.2
Genetic Algorithm for Solving Site Layout Problem
..........350
10.6
Image Processing
..............................................352
10.6.1
Designing Texture Filters with Genetic Algorithms
...........352
10.6.2
Genetic Algorithm Based Knowledge Acquisition
on Image Processing
.....................................357
10.6.3
Object Localization in Images Using Genetic Algorithm
.......362
10.6.4
Problem Description
.....................................363
10.6.5
Image Preprocessing
.....................................364
10.6.6
The Proposed Genetic Algorithm Approach
.................365
10.7
Data Mining
..................................................367
10.7.1
A Genetic Algorithm for Feature Selection in Data-Mining
.... 367
10.7.2
Genetic Algorithm Based Fuzzy Data Mining
to Intrusion Detection
....................................370
10.7.3
Selection and Partitioning of Attributes in Large-Scale Data
Mining Problems Using Genetic Algorithm
.................379
10.8
Wireless Networks
.............................................386
10.8.1
Genetic Algorithms for Topology Planning
in Wireless Networks
....................................386
10.8.2
Genetic Algorithm for Wireless ATM Network
...............387
10.9
Very Large Scale Integration (VLSI)
..............................395
10.9.1 Development
of a Genetic Algorithm Technique
for VLSI Testing
........................................395
10.9.2
VLSI Macro Cell Layout Using Hybrid GA
.................397
10.9.3
Problem Description
.....................................398
10.9.4
Genetic Layout Optimization
..............................399
10.10
Summary
.....................................................402
11
Introduction to Particle Swarm Optimization and Ant Colony
Optimization
...................................................403
11.1
Introduction
..................................................403
11.2
Particle Swarm Optimization
....................................403
11.2.1
Background of Particle Swarm Optimization
................404
11.2.2
Operation of Particle Swarm Optimization
..................405
11.2.3
Basic Flow of Particle Swarm Optimization
.................407
11.2.4
Comparison Between PSO and GA
........................408
11.2.5
Applications of PSO
.....................................410
11.3
Ant Colony Optimization
.......................................410
11.3.1
Biological Inspiration
....................................410
11.3.2
Similarities and Differences Between Real Ants
and Artificial Ants
.......................................414
11.3.3
Characteristics of Ant Colony Optimization
.................415
11.3.4
Ant Colony Optimization Algorithms
.......................416
11.3.5
Applications of Ant Colony Optimization
...................422
11.4
Summary
.....................................................424
Exercise Problems
.............................................424
Bibliography
.......................................................425
|
| adam_txt |
Contents
1
Evolutionary Computation
. 1
1.1
Introduction
. 1
1.2
The Historical Development of EC
. 2
1.2.1
Genetic Algorithms
. 2
1.2.2
Genetic Programming
. 3
1.2.3
Evolutionary Strategies
. 4
1.2.4
Evolutionary Programming
. 5
1.3
Features of Evolutionary Computation
. 5
1.3.1
Particulate Genes and Population Genetics
. 6
1.3.2
The Adaptive Code Book
. 7
1.3.3
The Genotype/Phenotype Dichotomy
. 8
1.4
Advantages of Evolutionary Computation
. 9
1.4.1
Conceptual Simplicity
. 10
1.4.2
Broad Applicability
. 10
1.4.3
Hybridization with Other Methods
. 11
1.4.4
Parallelism
. 11
1.4.5
Robust to Dynamic Changes
. 11
1.4.6
Solves Problems that have no Solutions
. 12
1.5
Applications of Evolutionary Computation
. 12
1.6
Summary
. 13
2
Genetic Algorithms
. 15
2.1
Introduction
. 15
2.2
Biological Background
. 16
2.2.1
The Cell
. 16
2.2.2
Chromosomes
. 16
2.2.3
Genetics
. 17
2.2.4
Reproduction
. 17
2.2.5
Natural Selection
. 19
2.3
What is Genetic Algorithm?
. 20
2.3.1
Search Space
. 20
2.3.2
Genetic Algorithms World
. 20
2.3.3
Evolution and Optimization
. 22
2.3.4
Evolution and Genetic Algorithms
. 23
2.4
Conventional Optimization and Search Techniques
. 24
2.4.1
Gradient-Based Local Optimization Method
. 25
2.4.2
Random Search
. 26
2.4.3
Stochastic Hill Climbing
. 27
2.4.4
Simulated Annealing
. 27
2.4.5
Symbolic Artificial Intelligence
(AI)
. 29
2.5
A Simple Genetic Algorithm
. 29
2.6
Comparison of Genetic Algorithm with Other
Optimization Techniques
. 33
2.7
Advantages and Limitations of Genetic Algorithm
. 34
2.8
Applications of Genetic Algorithm
. 35
2.9
Summary
. 36
3
Terminologies and Operators of GA
. 39
3.1
Introduction
. 39
3.2
Key Elements
. 39
3.3
Individuals
. 39
3.4
Genes
. 40
3.5
Fitness
. 41
3.6
Populations
. 41
3.7
Data Structures
. 42
3.8
Search Strategies
. 43
3.9
Encoding
. 43
3.9.1
Binary Encoding
. 43
3.9.2
Octal Encoding
. 44
3.9.3
Hexadecimal Encoding
. 44
3.9.4
Permutation Encoding (Real Number Coding)
. 44
3.9.5
Value Encoding
. 45
3.9.6
Tree Encoding
. 45
3.10
Breeding
. 46
3.10.1
Selection
. 46
3.10.2
Crossover (Recombination)
. 50
3.10.3
Mutation
. 56
3.10.4
Replacement
. 57
3.11
Search Termination (Convergence Criteria)
. 59
3.11.1
Best Individual
. 59
3.11.2
Worst individual
. 60
3.11.3
Sum of Fitness
. 60
3.11.4
Median Fitness
. 60
3.12
Why do Genetic Algorithms Work?
. 60
3.12.1
Building Block Hypothesis
. 61
3.12.2
A Macro-Mutation Hypothesis
. 62
3.12.3
An Adaptive Mutation Hypothesis
. 62
3.12.4
The Schema Theorem
. 63
3.12.5
Optimal Allocation of Trials
. 65
3.12.6
Implicit
Parallelism
. 66
3.12.7
The No Free Lunch Theorem
. 68
3.13
Solution Evaluation
. 68
3.14
Search Refinement
. 69
3.15
Constraints
. 69
3.16
Fitness Scaling
. 70
3.16.1
Linear Scaling
. 70
3.16.2
Sigma Truncation
. 71
3.16.3
Power Law Scaling
. 72
3.17
Example Problems
. 72
3.17.1
Maximizing a Function
. 72
3.17.2
Traveling Salesman Problem
. 76
3.18
Summary
. 78
Exercise Problems
. 81
4
Advanced Operators and Techniques in Genetic Algorithm
. 83
4.1
Introduction
. 83
4.2
Diploidy, Dominance and Abeyance
. 83
4.3
Multiploid
. 85
4.4
Inversion and Reordering
. 86
4.4.1
Partially Matched Crossover (PMX)
. 88
4.4.2
Order Crossover (OX)
. 88
4.4.3
Cycle Crossover (CX)
. 89
4.5
Niche and Speciation
. 89
4.5.1
Niche and Speciation in
Multimodal
Problems
. 90
4.5.2
Niche and Speciation in Unimodal Problems
. 93
4.5.3
Restricted Mating
. 96
4.6
Few Micro-operators
. 97
4.6.1
Segregation and
Translocation
. 97
4.6.2
Duplication and Deletion
. 97
4.6.3
Sexual Determination
. 98
4.7
Non-binary Representation
. 98
4.8
Multi-Objective Optimization
. 99
4.9
Combinatorial Optimizations
.100
4.10
Knowledge Based Techniques
.100
4.11
Summary
.102
Exercise Problems
.103
5
Classification of Genetic Algorithm
.105
5.1
Introduction
.105
5.2
Simple Genetic Algorithm (SGA)
.105
5.3
Parallel and Distributed Genetic Algorithm (PGA and DGA)
.106
5.3.1
Master-Slave Parallelization
.109
5.3.2
Fine Grained Parallel GAs (Cellular GAs)
.110
5.3.3
Multiple-Deme Parallel GAs (Distributed GAs or Coarse
Grained GAs)
.
Ill
5.3.4
Hierarchical Parallel Algorithms
.113
5.4
Hybrid Genetic Algorithm (HGA)
.115
5.4.1
Crossover
.116
5.4.2
Initialization Heuristics
.117
5.4.3
The RemoveSharp Algorithm
.117
5.4.4
The LocalOpt Algorithm
.119
5.5
Adaptive Genetic Algorithm
(AGA)
.119
5.5.1
Initialization
.120
5.5.2
Evaluation Function
.120
5.5.3
Selection operator
.121
5.5.4
Crossover operator
.121
5.5.5
Mutation operator
.122
5.6
Fast Messy Genetic Algorithm (FmGA)
.122
5.6.1
Competitive Template (CT) Generation
.123
5.7
Independent Sampling Genetic Algorithm (ISGA)
.124
5.7.1
Independent Sampling Phase
.125
5.7.2
Breeding Phase
.126
5.8
Summary
.127
Exercise Problems
.129
6
Genetic Programming
.131
6.1
Introduction
.131
6.2
Comparison of GP with Other Approaches
.131
6.3
Primitives of Genetic Programming
.135
6.3.1
Genetic Operators
.136
6.3.2
Generational Genetic Programming
.136
6.3.3
Tree Based Genetic Programming
.136
6.3.4
Representation of Genetic Programming
.137
6.4
Attributes in Genetic Programming
.141
6.5
Steps of Genetic Programming
.143
6.5.1
Preparatory Steps of Genetic Programming
.143
6.5.2
Executional Steps of Genetic Programming
.146
6.6
Characteristics of Genetic Programming
.149
6.6.1
What We Mean by "Human-Competitive"
.149
6.6.2
What We Mean by "High-Return"
.152
6.6.3
What We Mean by "Routine"
.154
6.6.4
What We Mean by "Machine Intelligence"
.154
6.7
Applications of Genetic Programming
.156
6.7.1
Applications of Genetic Programming
in Civil Engineering
.156
6.8
Haploid Genetic Programming with Dominance
.159
6.8.1
Single-Node Dominance Crossover
.161
6.8.2
Sub-Tree Dominance Crossover
.161
6.9
Summary
.161
Exercise Problems
.163
7
Genetic
Algorithm Optimization Problems
.165
7.1
Introduction
.165
7.2
Fuzzy Optimization Problems
.165
7.2.1
Fuzzy Multiobjective Optimization
.166
7.2.2
Interactive Fuzzy Optimization Method
.168
7.2.3
Genetic Fuzzy Systems
.168
7.3
Multiobjective Reliability Design Problem
.170
7.3.1
Network Reliability Design
.170
7.3.2
Bicriteria Reliability Design
.174
7.4
Combinatorial Optimization Problem
.176
7.4.1
Linear Integer Model
.178
7.4.2
Applications of Combinatorial Optimization
.179
7.4.3
Methods
.182
7.5
Scheduling Problems
.187
7.5.1
Genetic Algorithm for Job Shop Scheduling Problems (JSSP)
. 187
7.6
Transportation Problems
.190
7.6.1
Genetic Algorithm in Solving Transportation
Location-Allocation Problems with Euclidean Distances
.191
7.6.2
Real-Coded Genetic Algorithm (RCGA) for Integer Linear
Programming in Production-Transportation Problems
with Flexible Transportation Cost
.194
7.7
Network Design and Routing Problems
.199
7.7.1
Planning of Passive Optical Networks
.199
7.7.2
Planning of Packet Switched Networks
.202
7.7.3
Optimal Topological Design of All Terminal Networks
.203
7.8
Summary
.208
Exercise Problems
.209
8
Genetic Algorithm Implementation Using
Matlab
.211
8.1
Introduction
.211
8.2
Data Structures
.211
8.2.1
Chromosomes
.212
8.2.2
Phenotypes
.212
8.2.3
Objective Function Values
.213
8.2.4
Fitness Values
.213
8.2.5
Multiple
Subpopulations
.213
8.3
Toolbox Functions
.214
8.4
Genetic Algorithm Graphical User Interface Toolbox
.219
8.5
Solved Problems using
MATLAB
.224
8.6
Summary
.260
Review Questions
.261
Exercise Problems
.261
9
Genetic Algorithm Optimization in
C/C++.263
9.1
Introduction
.263
9.2
Traveling Salesman Problem (TSP)
.263
9.3
Word Matching Problem
.271
9.4
Prisoner's Dilemma
.280
9.5
Maximize f(x)
=
x2
.286
9.6
Minimization a Sine Function with Constraints
.292
9.6.1
Problem Description
.293
9.7
Maximizing the Function f(x)
=
x*sin(l(№*x)
+ 10.302
9.8
Quadratic Equation Solving
.310
9.9
Summary
.315
9.9.1
Projects
.315
10
Applications of Genetic Algorithms
.317
10.1
Introduction
.317
10.2
Mechanical Sector
.317
10.2.1
Optimizing Cyclic-Steam Oil Production
with Genetic Algorithms
.317
10.2.2
Genetic Programming and Genetic Algorithms
for Auto-tuning Mobile Robot Motion Control
.320
10.3
Electrical Engineering
.324
10.3.1
Genetic Algorithms in Network Synthesis
.324
10.3.2
Genetic Algorithm Tools for Control Systems Engineering
.328
10.3.3
Genetic Algorithm Based Fuzzy Controller for Speed Control
of Brushless DC Motor
.334
10.4
Machine Learning
.341
10.4.1
Feature Selection in Machine learning using GA
.341
10.5
Civil Engineering
.345
10.5.1
Genetic Algorithm as Automatic Structural Design Tool
.345
10.5.2
Genetic Algorithm for Solving Site Layout Problem
.350
10.6
Image Processing
.352
10.6.1
Designing Texture Filters with Genetic Algorithms
.352
10.6.2
Genetic Algorithm Based Knowledge Acquisition
on Image Processing
.357
10.6.3
Object Localization in Images Using Genetic Algorithm
.362
10.6.4
Problem Description
.363
10.6.5
Image Preprocessing
.364
10.6.6
The Proposed Genetic Algorithm Approach
.365
10.7
Data Mining
.367
10.7.1
A Genetic Algorithm for Feature Selection in Data-Mining
. 367
10.7.2
Genetic Algorithm Based Fuzzy Data Mining
to Intrusion Detection
.370
10.7.3
Selection and Partitioning of Attributes in Large-Scale Data
Mining Problems Using Genetic Algorithm
.379
10.8
Wireless Networks
.386
10.8.1
Genetic Algorithms for Topology Planning
in Wireless Networks
.386
10.8.2
Genetic Algorithm for Wireless ATM Network
.387
10.9
Very Large Scale Integration (VLSI)
.395
10.9.1 Development
of a Genetic Algorithm Technique
for VLSI Testing
.395
10.9.2
VLSI Macro Cell Layout Using Hybrid GA
.397
10.9.3
Problem Description
.398
10.9.4
Genetic Layout Optimization
.399
10.10
Summary
.402
11
Introduction to Particle Swarm Optimization and Ant Colony
Optimization
.403
11.1
Introduction
.403
11.2
Particle Swarm Optimization
.403
11.2.1
Background of Particle Swarm Optimization
.404
11.2.2
Operation of Particle Swarm Optimization
.405
11.2.3
Basic Flow of Particle Swarm Optimization
.407
11.2.4
Comparison Between PSO and GA
.408
11.2.5
Applications of PSO
.410
11.3
Ant Colony Optimization
.410
11.3.1
Biological Inspiration
.410
11.3.2
Similarities and Differences Between Real Ants
and Artificial Ants
.414
11.3.3
Characteristics of Ant Colony Optimization
.415
11.3.4
Ant Colony Optimization Algorithms
.416
11.3.5
Applications of Ant Colony Optimization
.422
11.4
Summary
.424
Exercise Problems
.424
Bibliography
.425 |
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| author | Sivanandam, S. N. Deepa, S. N. |
| author_GND | (DE-588)132409747 |
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| callnumber-first | Q - Science |
| callnumber-label | QA76 |
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| callnumber-sort | QA 276.623 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 134 ST 300 ST 301 |
| classification_tum | DAT 718f |
| ctrlnum | (OCoLC)166357929 (DE-599)BVBBV035042190 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| format | Book |
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| id | DE-604.BV035042190 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:53:32Z |
| indexdate | 2024-07-09T21:20:52Z |
| institution | BVB |
| isbn | 9783540731894 354073189X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016710993 |
| oclc_num | 166357929 |
| open_access_boolean | |
| owner | DE-739 DE-703 DE-11 DE-91G DE-BY-TUM |
| owner_facet | DE-739 DE-703 DE-11 DE-91G DE-BY-TUM |
| physical | XIX, 442 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| spelling | Sivanandam, S. N. Verfasser (DE-588)132409747 aut Introduction to genetic algorithms S. N. Sivanandam ; S. N. Deepa Berlin [u.a.] Springer 2008 XIX, 442 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Algorithmes génétiques Programmation génétique (Informatique) Genetic algorithms Genetic programming (Computer science) Genetischer Algorithmus (DE-588)4265092-6 gnd rswk-swf Genetischer Algorithmus (DE-588)4265092-6 s DE-604 Deepa, S. N. Verfasser aut Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016710993&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sivanandam, S. N. Deepa, S. N. Introduction to genetic algorithms Algorithmes génétiques Programmation génétique (Informatique) Genetic algorithms Genetic programming (Computer science) Genetischer Algorithmus (DE-588)4265092-6 gnd |
| subject_GND | (DE-588)4265092-6 |
| title | Introduction to genetic algorithms |
| title_auth | Introduction to genetic algorithms |
| title_exact_search | Introduction to genetic algorithms |
| title_exact_search_txtP | Introduction to genetic algorithms |
| title_full | Introduction to genetic algorithms S. N. Sivanandam ; S. N. Deepa |
| title_fullStr | Introduction to genetic algorithms S. N. Sivanandam ; S. N. Deepa |
| title_full_unstemmed | Introduction to genetic algorithms S. N. Sivanandam ; S. N. Deepa |
| title_short | Introduction to genetic algorithms |
| title_sort | introduction to genetic algorithms |
| topic | Algorithmes génétiques Programmation génétique (Informatique) Genetic algorithms Genetic programming (Computer science) Genetischer Algorithmus (DE-588)4265092-6 gnd |
| topic_facet | Algorithmes génétiques Programmation génétique (Informatique) Genetic algorithms Genetic programming (Computer science) Genetischer Algorithmus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016710993&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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