Efficient and Accurate Parallel Genetic Algorithms

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Springer Science & Business Media, Nov 30, 2000 - Computers - 162 pages
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As genetic algorithms (GAs) become increasingly popular, they are applied to difficult problems that may require considerable computations. In such cases, parallel implementations of GAs become necessary to reach high-quality solutions in reasonable times. But, even though their mechanics are simple, parallel GAs are complex non-linear algorithms that are controlled by many parameters, which are not well understood.
Efficient and Accurate Parallel Genetic Algorithms is about the design of parallel GAs. It presents theoretical developments that improve our understanding of the effect of the algorithm's parameters on its search for quality and efficiency. These developments are used to formulate guidelines on how to choose the parameter values that minimize the execution time while consistently reaching solutions of high quality.
Efficient and Accurate Parallel Genetic Algorithms can be read in several ways, depending on the readers' interests and their previous knowledge about these algorithms. Newcomers to the field will find the background material in each chapter useful to become acquainted with previous work, and to understand the problems that must be faced to design efficient and reliable algorithms. Potential users of parallel GAs that may have doubts about their practicality or reliability may be more confident after reading this book and understanding the algorithms better. Those who are ready to try a parallel GA on their applications may choose to skim through the background material, and use the results directly without following the derivations in detail. These readers will find that using the results can help them to choose the type of parallel GA that best suits their needs, without having to invest the time to implement and test various options. Once that is settled, even the most experienced users dread the long and frustrating experience of configuring their algorithms by trial and error. The guidelines contained herein will shorten dramatically the time spent tweaking the algorithm, although some experimentation may still be needed for fine-tuning.
Efficient and Accurate Parallel Genetic Algorithms is suitable as a secondary text for a graduate level course, and as a reference for researchers and practitioners in industry.
  

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Contents

INTRODUCTION
1
1 AN INTRODUCTION TO GENETIC ALGORITHMS
2
2 A CLASSIFICATION OF PARALLEL GAs
6
3 SUMMARY
11
THE GAMBLERS RUIN PROBLEM AND POPULATION SIZING
13
1 BACKGROUND
14
2 DECIDING WELL BETWEEN TWO BBs
18
3 THE GAMBLERS RUIN MODEL
20
3 ARBITRARY TOPOLOGIES
74
4 EXPERIMENTS
77
5 SUMMARY
80
MIGRATION RATES AND OPTIMAL TOPOLOGIES
81
1 DEGREE OF CONNECTIVITY
82
2 MULTIPLE EPOCHS AND CHOOSING A TOPOLOGY
87
3 PARALLEL DEMES IN THE LONG RUN
94
4 SUMMARY
96

4 EXPERIMENTAL VERIFICATION
23
5 NOISE AND POPULATION SIZING
28
6 THE EFFECT OF SELECTION PRESSURE
30
7 SUMMARY
31
MASTERSLAVE PARALLEL GENETIC ALGORITHMS
33
1 BACKGROUND
34
2 SYNCHRONOUS MASTERSLAVES
36
3 EXPERIMENTS
42
4 ASYNCHRONOUS MASTERSLAVES
43
5 A DISTRIBUTED PANMICTIC POPULATION
45
6 SUMMARY
47
BOUNDING CASES OF GENETIC ALGORITHMS WITH MULTIPLE DEMES
49
1 BACKGROUND
50
2 PARALLEL SPEEDUPS
54
3 ISOLATED DEMES
55
4 FULLYCONNECTED DEMES
58
5 SUMMARY
64
MARKOV CHAIN MODELS OF MULTIPLE DEMES
67
1 FULLYCONNECTED DEMES WITH MAXIMUM MIGRATION RATES
68
2 ARBITRARY MIGRATION RATES
73
MIGRATION SELECTION PRESSURE AND SUPERLINEAR SPEEDUPS
97
1 SELECTION PRESSURE
98
2 TAKEOVER TIMES
100
3 SELECTION INTENSITY
102
4 EXPERIMENTS
111
5 SUPERLINEAR SPEEDUPS
114
6 VARIANCE AND THE HIGHER CUMULANTS
117
7 SUMMARY
119
FINEGRAINED AND HIERARCHICAL PARALLEL GENETIC ALGORITHMS
121
1 FINEGRAINED PARALLEL GAs
122
2 HIERARCHICAL PARALLEL GAs
126
3 OPTIMAL HIERARCHICAL PARALLEL GAs
129
4 AN EXAMPLE OF OPTIMAL DESIGN
131
5 SUMMARY
134
SUMMARY EXTENSIONS AND CONCLUSIONS
135
2 EXTENSIONS
139
3 CONCLUSIONS
141
References
145
Index
159
Copyright

Common terms and phrases

Popular passages

Page 149 - Sizing populations for serial and parallel genetic algorithms. In Schaffer, JD (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (pp.
Page 145 - In Grefenstette, JJ (Ed.), Proceedings of the Second International Conference on Genetic Algorithms (pp. 14-21).
Page 145 - A Parallel Genetic Algorithm for Solving the School Timetabling Problem", In Proceedings of the Fifteenth Australian Computer Science Conference (ACSC-15), Volume 14, pp 1-11, 1992.
Page 154 - In Banzhaf, W., Daida, J., Eiben, AE, Garzon, MH, Honavar, V., Jakiela, M., & Smith, RE (eds.). GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference (pp.

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