Evolutionary Algorithms for Food Science and Technology

Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis.

Evelyne Lutton, INRA.

Nathalie Perrot, INRA.

Alberto Tonda, INRA.

Preface  xi

Chapter 1. Introduction 1

1.1. Evolutionary computation in food science and technology  1

1.2. A panorama of the current use of evolutionary algorithms in the domain  2

1.3. The purpose of this book  6

Chapter 2. A Brief Introduction to Evolutionary Algorithms  7

2.1. Artificial evolution: Darwin’s theory in a computer 8

2.2. The source of inspiration: evolutionism and Darwin’s theory 10

2.3. Darwin in a computer  12

2.4. The genetic engine 14

2.4.1. Evolutionary loop  14

2.4.2. Genetic operators  17

2.4.3. GAs and binary representation  17

2.4.4. ESs and continuous representation  18

2.4.5. GP and tree-based representation 20

2.4.6. GE and grammar-based representation  23

2.4.7. Selective pressure  23

2.5. Theoretical issues  24

2.6. Beyond optimization  26

2.6.1. Multimodal landscapes 26

2.6.2. Co-evolution 27

2.6.3. Multiobjective optimization  29

2.6.4. Interactive optimization  31

Chapter 3. Model Analysis and Visualization 33

3.1. Introduction 33

3.1.1. Experimental data  37

3.1.2. Modeling milk gel competition at the interface 39

3.1.3. Learning the parameters of the model using an evolutionary approach 41

3.1.4. Visualization using the GraphDice environment 43

3.2. Results and discussion 45

3.2.1. Sensitivity analysis 45

3.2.2. Visual exploration of the model  46

3.2.3. Theoretical discussion 48

3.3. Conclusions 53

3.4. Acknowledgments  55

Chapter 4. Interactive Model Learning 57

4.1. Introduction 58

4.2. Background 59

4.2.1. Bayesian networks 59

4.2.2. The structure learning problem  60

4.2.3. Visualizing BNs 63

4.3. Proposed approach 63

4.4. Experimental setup 66

4.5. Analysis and perspectives 67

4.6. Conclusion . 70

Chapter 5. Modeling Human Expertise Using Genetic Programming  71

5.1. Cooperative co-evolution  72

5.2. Modeling agrifood industrial processes  73

5.2.1. The Camembert cheese-ripening process 74

5.2.2. Modeling expertise on cheese ripening  77

5.3. Phase estimation using GP 77

5.3.1. Phase estimation using a classical GP  77

5.3.2. Phase estimation using a Parisian GP 81

5.3.3. Variable population size strategies in a Parisian GP 86

5.3.4. Analysis 98

5.4. Bayesian network structure learning using CCEAs 99

5.4.1. Recalling some probability notions  99

5.4.2. Bayesian networks 100

5.4.3. Evolution of an IM 105

5.4.4. Sharing  109

5.4.5. Immortal archive and embossing points 110

5.4.6. Description of the main parameters  111

5.4.7. BN structure estimation  112

5.4.8. Experiments and results  114

5.4.9. Analysis 122

5.5. Conclusion  123

Conclusion 125

Bibliography  127

Index 149

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