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Tables of Contents for Evolutionary Optimization
Chapter/Section Title
Page #
Page Count
Preface
ix
Contributing Authors
xi
Part I Introduction
Conventional Optimization Techniques
Mark S. Hillier and Frederick S. Hillier
3
24
Classifying Optimization Models
4
2
Linear Programming
6
3
Goal Programming
9
1
Integer Programming
10
3
Nonlinear Programming
13
9
Simulation
22
3
Further Reading
25
2
Evolutionary Computation
Xin Yao
27
30
What Is Evolutionary Computation
27
8
A Brief Overview of Evolutionary Computation
35
4
Evolutionary Algorithm and Generate-and-Test Search Algorithm
39
1
Search Operators
40
6
Summary
46
11
Part II Single Objective Optimization
Evolutionary Algorithms and Constrained Optimization
Zbigniew Michalewicz and Martin Schmidt
57
30
Introduction
57
1
General considerations
58
10
Numerical optimization
68
11
Final Remarks
79
8
Constrained Evolutionary Optimization
Thomas Runarsson and Xin Yao
87
30
Introduction
87
2
The Penalty Function Method
89
4
Stochastic Ranking
93
2
Global Competitive Ranking
95
2
How Penalty Methods Work
97
3
Experimental Study
100
6
Conclusion
106
11
Appendix: Test Function Suite
109
8
Part III Multi-Objective Optimization
Evolutionary Multiobjective Optimization
Carlos A. Coello Coello
117
30
Introduction
118
1
Definitions
118
1
Historical Roots
119
2
A Quick Survey of EMOO Approaches
121
7
Current Research
128
6
Future Research Paths
134
1
Summary
135
12
MEA for Engineering Shape Design
Kalyanmoy Deb and Tushar Goel
147
30
Introduction
147
2
Multi-Objective Optimization and Pareto-Optimality
149
2
Elitist Non-dominated Sorting GA (NSGA-II)
151
4
Hybrid Approach
155
4
Optimal Shape Design
159
3
Simulation Results
162
10
Conclusion
172
5
Assessment Methodologies for MEAs
Ruhul Sarker and Carlos A. Coello Coello
177
22
Introduction
177
1
Assessment Methodologies
178
8
Discussion
186
2
Comparing Two Algorithms: An Example
188
3
Conclusions and Future Research Paths
191
8
Part IV Hybrid Algorithms
Hybrid Genetic Algorithms
Jeffrey A. Joines and Michael G. Kay
199
30
Introduction
199
3
Hybridizing GAs with Local Improvement Procedures
202
16
Adaptive Memory GA's
218
7
Summary
225
4
Combining choices of heuristics
Peter Ross and Emma Hart
229
24
Introduction
229
3
GAs and parameterised algorithms
232
3
Job Shop Scheduling
235
6
Scheduling chicken catching
241
3
Timetabling
244
4
Discussion and future directions
248
5
Nonlinear Constrained Optimization
Benjamin W. Wah and Yi-Xin Chen
253
26
Introduction
253
4
Previous Work
257
6
A General Framework to look for SPdn
263
5
Experimental Results
268
5
Conclusions
273
6
Part V Parameter Selection in EAs
Parameter Selection
Zbigniew Michalewicz Agoston E. Eiben and Robert Hinterding
279
30
Introduction
279
2
Parameter tuning vs. parameter control
281
3
An example
284
6
Classification of Control Techniques
290
4
Various forms of control
294
3
Discussion
297
12
Part VI Application of EAs to Practical Problems
Design of Production Facilities
Alice E. Smith and Bryan A. Norman
309
20
Introduction
309
3
Design for Material Flow When the Number of I/O Points is Unconstrained
312
3
Design for Material Flow for a Single I/O Point
315
3
Considering Intradepartmental Flow
318
3
Material Handling System Design
321
2
Concluding Remarks
323
6
Virtual Population and Acceleration Techniques
Kit Po Wong and An Li
329
20
Introduction
329
2
Concept of Virtual Population
331
1
Solution Acceleration Techniques
332
2
Accelerated GA and Acceleration Schemes
334
1
Validation of Methods
335
1
Further Improvement: Refined Scheme (c)
336
1
The Load Flow Problem in Electrical Power Networks
337
1
Accelerated Constrained Genetic Algorithms for Load Flow Calculation
338
1
Klos-Kerner 11-Node System Studies
339
4
Conclusions
343
6
Part VII Applciation of EAs to Theoretical Problems
Methods for the analysis of EAs on pseudo-boolean functions
Ingo Wegener
349
22
Introduction
349
2
Optimization of Pseudo-boolean functions
351
1
Performance measures
352
1
Selected functions
353
2
Tail inequalities
355
2
The coupon collector's theorem
357
1
The gambler's ruin problem
358
1
Upper bounds by artificial fitness levels
359
3
Lower bounds by artificial fitness levels
362
1
Potential functions
363
2
Investigations of typical runs
365
6
A GA Heuristic For Finite Horizon POMDPs
Alex Z.-Z. Lin James C. Bean and Chelsea C. White III
371
28
Introduction
371
1
Partially Observed MDP
372
4
Basics of Genetic Algorithms
376
4
Proposed Genetic Algorithm Heuristic
380
7
Heuristic Performance Measures
387
3
Numerical Results
390
1
Summary
391
8
Appendix
397
2
Finding Good k-Tree Subgraphs
Elham Ghashghai and Ronald L. Rardin
399
16
Introduction
399
1
k-Trees
400
1
Algorithm Paradigm and Terminology
401
2
Genetic Algorithm Implementation
403
3
Computational Results
406
6
Concluding Remarks and Further Research
412
3
Index
415
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