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E-raamat: Machine Learning for Evolution Strategies

  • Formaat: PDF+DRM
  • Sari: Studies in Big Data 20
  • Ilmumisaeg: 25-May-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319333830
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Machine Learning for Evolution StrategiesSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Studies in Big Data 20
  • Ilmumisaeg: 25-May-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319333830
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This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. The set ofmethods comprises covariance matrix estimation, meta-modeling of fitness andconstraint functions, dimensionality reduction for search and visualization ofhigh-dimensional optimization processes, and clustering-based niching. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Experiments mostly employ a (1+1)-ES and are implemented in Pythonusing the machine learning library scikit-learn. The examples are conducted ontypical benchmark problems illustrating algorithmic concepts and theirexperimental behavior. The book closes with a discussion of related lines ofresearch.

Part I Evolution Strategies.- Part II Machine Learning.- Part III Supervised Learning.
1 Introduction
1(12)
1.1 Computational Intelligence
1(1)
1.2 Optimization
1(2)
1.3 Machine Learning and Big Data
3(2)
1.4 Motivation
5(1)
1.5 Benchmark Problems
5(1)
1.6 Overview
6(2)
1.7 Previous Work
8(1)
1.8 Notations
8(1)
1.9 Python
9(4)
References
10(3)
Part I Evolution Strategies
2 Evolution Strategies
13(10)
2.1 Introduction
13(1)
2.2 Evolutionary Algorithms
14(1)
2.3 History
15(1)
2.4 Recombination
16(1)
2.5 Mutation
16(1)
2.6 Selection
17(1)
2.7 Rechenberg's 1/5th Success Rule
18(1)
2.8 (1+1)-ES
19(1)
2.9 Conclusions
20(3)
References
21(2)
3 Covariance Matrix Estimation
23(12)
3.1 Introduction
23(1)
3.2 Covariance Matrix Estimation
24(1)
3.3 Algorithm
25(1)
3.4 Related Work
26(1)
3.5 Experimental Analysis
27(3)
3.6 Conclusions
30(5)
References
31(4)
Part II Machine Learning
4 Machine Learning
35(10)
4.1 Introduction
35(1)
4.2 Prediction and Inference
36(1)
4.3 Classification
37(1)
4.4 Model Selection
38(1)
4.5 Curse of Dimensionality
39(1)
4.6 Bias-Variance Trade-Off
40(1)
4.7 Feature Selection and Extraction
41(1)
4.8 Conclusions
42(3)
References
43(2)
5 Scikit-Learn
45(12)
5.1 Introduction
45(1)
5.2 Data Management
46(1)
5.3 Supervised Learning
47(1)
5.4 Pre-processing Methods
48(1)
5.5 Model Evaluation
49(1)
5.6 Model Selection
50(1)
5.7 Unsupervised Learning
51(1)
5.8 Conclusions
52(5)
Reference
53(4)
Part III Supervised Learning
6 Fitness Meta-Modeling
57(10)
6.1 Introduction
57(1)
6.2 Nearest Neighbors
58(1)
6.3 Algorithm
59(1)
6.4 Related Work
60(1)
6.5 Experimental Analysis
61(3)
6.6 Conclusions
64(3)
References
64(3)
7 Constraint Meta-Modeling
67(12)
7.1 Introduction
67(1)
7.2 Support Vector Machines
68(3)
7.3 Algorithm
71(1)
7.4 Related Work
72(1)
7.5 Experimental Analysis
73(2)
7.6 Conclusions
75(4)
References
75(4)
Part IV Unsupervised Learning
8 Dimensionality Reduction Optimization
79(10)
8.1 Introduction
79(1)
8.2 Dimensionality Reduction
80(1)
8.3 Principal Component Analysis
80(2)
8.4 Algorithm
82(1)
8.5 Related Work
83(1)
8.6 Experimental Analysis
84(2)
8.7 Conclusions
86(3)
References
87(2)
9 Solution Space Visualization
89(10)
9.1 Introduction
89(1)
9.2 Isometric Mapping
90(2)
9.3 Algorithm
92(1)
9.4 Related Work
93(1)
9.5 Experimental Analysis
94(2)
9.6 Conclusions
96(3)
References
97(2)
10 Clustering-Based Niching
99(12)
10.1 Introduction
99(1)
10.2 Clustering
100(1)
10.3 Algorithm
101(1)
10.4 Related Work
102(1)
10.5 Experimental Analysis
103(3)
10.6 Conclusions
106(5)
References
106(5)
Part V Ending
11 Summary and Outlook
111(5)
11.1 Summary
111(2)
11.2 Evolutionary Computation for Machine Learning
113(2)
11.3 Outlook
115(1)
References 116(3)
Appendix A Benchmark Functions 119(4)
Index 123
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