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Evolutionary Optimization in Dynamic Environments : Genetic Algorithms and Evolutionary Computation - Jurgen Branke

Evolutionary Optimization in Dynamic Environments

Genetic Algorithms and Evolutionary Computation

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Published: 31st December 2001
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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.

Prefacep. xi
Brief Introduction to Evolutionary Algorithmsp. 1
From Biology to Softwarep. 1
Basic Evolutionary Algorithmp. 4
Further Aspectsp. 7
Representationp. 7
Parallelizationp. 8
Runtime Comparisonsp. 10
Enabling Continuous Adaptation
Optimization in Dynamic Environmentsp. 13
Categorization of Dynamic Environmentsp. 14
Suitable Benchmark Problemsp. 17
Dynamic Bit-Matchingp. 17
Moving Parabolap. 18
Time-Varying Knapsack Problemp. 19
Moving Peaks Functionp. 20
Scheduling Problemsp. 24
Oscillating Peaksp. 25
Measuring Performancep. 26
Detecting Changes in the Environmentp. 28
Survey: State of The Artp. 31
Restart / Re-Initializationp. 31
Adapting Mutationp. 34
Implicit or Explicit Memoryp. 38
Modifying Selectionp. 42
Multi-Population Approachesp. 44
Self-Organizing Scoutsp. 44
Shifting Balance GAp. 45
Multinational GAp. 45
Other Approachesp. 46
Immune Systemsp. 46
Parallel EA Variantsp. 46
Evolving Control Rulesp. 46
Modeling the Systemp. 47
Stochastic Genetic Algorithmp. 47
Clan-based Evolutionp. 48
Dual and Folding Genetic Algorithmp. 48
Further Aspectsp. 49
Steady-State or Generational Replacement?p. 49
Darwinian vs. Lamarckian Learningp. 50
Parameter Settingsp. 51
Other Related Workp. 51
From Memory to Self-Organizationp. 53
Memory/Searchp. 54
General Thoughts about Memoryp. 54
The Best of Two Worldsp. 56
Self-Organizing Scoutsp. 58
Empirical Evaluationp. 67
General Remarks on the Experimental Setupp. 67
Default Parameter Settingsp. 69
Oscillating Peaks Functionp. 71
Standard Test Casep. 72
The Influence of Change Frequencyp. 76
Non-vanishing Peaksp. 78
Moving Peaks Functionp. 81
Sensitivity of Parameter Settingsp. 81
The Effect of Peaks Movementsp. 86
Changing the Number of Peaksp. 90
The Influence of Change Frequencyp. 94
Higher Dimensionalityp. 96
Correlation of Shiftsp. 97
Summary of Part 1p. 99
Considering Adaptation Cost
Adaptation Cost Vs. Solution Qualityp. 105
Introduction to Multi-Objective EAsp. 106
Related Workp. 109
Guided Multi Objective Evolutionary Algorithmp. 111
Experimental Resultsp. 114
Summary of Chapter 7p. 121
Robustness and Flexibility--Precaution against Changes
Searching for Robust Solutionsp. 125
Motivationp. 125
Related Workp. 128
Test Problemsp. 132
Experimental Setup and Default Parametersp. 136
How to select the final solution?p. 138
Influence of Several EA Parametersp. 141
The Number of Samples Throughout the Runp. 141
Allowed Running Timep. 142
Selection Pressurep. 143
Steady State vs. Generational Reproductionp. 144
Population Sizep. 147
The Island Modelp. 153
Selection Methodp. 154
Evaluating Good Individuals More Oftenp. 156
Minimizing the Estimation Errorp. 158
Better Sampling Methodsp. 160
Changing the Sample Sizep. 163
Looking at Other Individuals in the Neighborhoodp. 167
Summary of Chapter 8p. 169
From Robustness to Flexibilityp. 173
Related Workp. 174
Dynamic Job Shop Schedulingp. 175
Decomposing Dynamic JSSPsp. 175
The Role of Schedule Buildersp. 176
A Flexibility Measure for Dynamic Stochastic JSSPsp. 178
Empirical Evaluationp. 180
Summary of Chapter 9p. 183
Summary and Outlookp. 185
Referencesp. 191
Indexp. 207
Table of Contents provided by Syndetics. All Rights Reserved.

ISBN: 9780792376316
ISBN-10: 0792376315
Series: Genetic Algorithms and Evolutionary Computation
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 208
Published: 31st December 2001
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 23.5 x 15.88  x 1.91
Weight (kg): 0.52