Evolutionary Optimization in Dynamic Environments

By: JÃ¼rgen Branke

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Hardcover | 31 December 2001

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Hardcover
228 Pages

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23.5 x 15.88 x 1.91

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Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. Evolutionary Optimization in Dynamic Environments is the first comprehensive work on the application of EAs to dynamic optimization problems. It provides an extensive survey on research in the area and shows how EAs can be successfully used to

continuously and efficiently adapt a solution to a changing environment,
find a good trade-off between solution quality and adaptation cost,
find robust solutions whose quality is insensitive to changes in the environment,
find flexible solutions which are not only good but that can be easily adapted when necessary.

All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes Evolutionary Optimization in Dynamic Environments an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms.

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Preface	p. xi
Brief Introduction to Evolutionary Algorithms	p. 1
From Biology to Software	p. 1
Basic Evolutionary Algorithm	p. 4
Further Aspects	p. 7
Representation	p. 7
Parallelization	p. 8
Runtime Comparisons	p. 10
Enabling Continuous Adaptation
Optimization in Dynamic Environments	p. 13
Categorization of Dynamic Environments	p. 14
Suitable Benchmark Problems	p. 17
Dynamic Bit-Matching	p. 17
Moving Parabola	p. 18
Time-Varying Knapsack Problem	p. 19
Moving Peaks Function	p. 20
Scheduling Problems	p. 24
Oscillating Peaks	p. 25
Measuring Performance	p. 26
Detecting Changes in the Environment	p. 28
Survey: State of The Art	p. 31
Restart / Re-Initialization	p. 31
Adapting Mutation	p. 34
Implicit or Explicit Memory	p. 38
Modifying Selection	p. 42
Multi-Population Approaches	p. 44
Self-Organizing Scouts	p. 44
Shifting Balance GA	p. 45
Multinational GA	p. 45
Other Approaches	p. 46
Immune Systems	p. 46
Parallel EA Variants	p. 46
Evolving Control Rules	p. 46
Modeling the System	p. 47
Stochastic Genetic Algorithm	p. 47
Clan-based Evolution	p. 48
Dual and Folding Genetic Algorithm	p. 48
Further Aspects	p. 49
Steady-State or Generational Replacement?	p. 49
Darwinian vs. Lamarckian Learning	p. 50
Parameter Settings	p. 51
Other Related Work	p. 51
From Memory to Self-Organization	p. 53
Memory/Search	p. 54
General Thoughts about Memory	p. 54
The Best of Two Worlds	p. 56
Self-Organizing Scouts	p. 58
Empirical Evaluation	p. 67
General Remarks on the Experimental Setup	p. 67
Default Parameter Settings	p. 69
Oscillating Peaks Function	p. 71
Standard Test Case	p. 72
The Influence of Change Frequency	p. 76
Non-vanishing Peaks	p. 78
Moving Peaks Function	p. 81
Sensitivity of Parameter Settings	p. 81
The Effect of Peaks Movements	p. 86
Changing the Number of Peaks	p. 90
The Influence of Change Frequency	p. 94
Higher Dimensionality	p. 96
Correlation of Shifts	p. 97
Summary of Part 1	p. 99
Considering Adaptation Cost
Adaptation Cost Vs. Solution Quality	p. 105
Introduction to Multi-Objective EAs	p. 106
Related Work	p. 109
Guided Multi Objective Evolutionary Algorithm	p. 111
Experimental Results	p. 114
Summary of Chapter 7	p. 121
Robustness and Flexibility--Precaution against Changes
Searching for Robust Solutions	p. 125
Motivation	p. 125
Related Work	p. 128
Test Problems	p. 132
Experimental Setup and Default Parameters	p. 136
How to select the final solution?	p. 138
Influence of Several EA Parameters	p. 141
The Number of Samples Throughout the Run	p. 141
Allowed Running Time	p. 142
Selection Pressure	p. 143
Steady State vs. Generational Reproduction	p. 144
Population Size	p. 147
The Island Model	p. 153
Selection Method	p. 154
Evaluating Good Individuals More Often	p. 156
Minimizing the Estimation Error	p. 158
Better Sampling Methods	p. 160
Changing the Sample Size	p. 163
Looking at Other Individuals in the Neighborhood	p. 167
Summary of Chapter 8	p. 169
From Robustness to Flexibility	p. 173
Related Work	p. 174
Dynamic Job Shop Scheduling	p. 175
Decomposing Dynamic JSSPs	p. 175
The Role of Schedule Builders	p. 176
A Flexibility Measure for Dynamic Stochastic JSSPs	p. 178
Empirical Evaluation	p. 180
Summary of Chapter 9	p. 183
Summary and Outlook	p. 185
References	p. 191
Index	p. 207
Table of Contents provided by Syndetics. All Rights Reserved.

Evolutionary Optimization in Dynamic Environments

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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