Cover -- Title Page -- Copyright -- Contents -- Acknowledgments -- Preface -- The sources -- Technique, power and language -- The human factor in computer science -- Optimization? -- Promises and limits of computational optimization -- The evaluation utopy -- Quantitative versus qualitative -- Issues with complex system -- Optimality in food science -- Slow optimization -- 1. Introduction -- 1.1. Evolutionary computation in food science and technology -- 1.2. A panorama of the current use of evolutionary algorithms in the domain -- 1.3. The purpose of this book -- 2. A Brief Introduction to Evolutionary Algorithms -- 2.1. Artificial evolution: Darwin's theory in a computer -- 2.2. The source of inspiration: evolutionism and Darwin's theory -- 2.3. Darwin in a computer -- 2.4. The genetic engine -- 2.4.1. Evolutionary loop -- 2.4.2. Genetic operators -- 2.4.3. GAs and binary representation -- 2.4.4. ESs and continuous representation -- 2.4.5. GP and tree-based representation -- 2.4.6. GE and grammar-based representation -- 2.4.7. Selective pressure -- 2.5. Theoretical issues -- 2.6. Beyond optimization -- 2.6.1. Multimodal landscapes -- 2.6.2. Co-evolution -- 2.6.3. Multiobjective optimization -- 2.6.4. Interactive optimization -- 3. Model Analysis and Visualization -- 3.1. Introduction -- 3.1.1. Experimental data -- 3.1.2. Modeling milk gel competition at the interface -- 3.1.3. Learning the parameters of the model using an evolutionary approach -- 3.1.4. Visualization using the GraphDice environment -- 3.2. Results and discussion -- 3.2.1. Sensitivity analysis -- 3.2.2. Visual exploration of the model -- 3.2.3. Theoretical discussion -- 3.3. Conclusions -- 3.4. Acknowledgments -- 4. Interactive Model Learning -- 4.1. Introduction -- 4.2. Background -- 4.2.1. Bayesian networks -- 4.2.2. The structure learning problem -- 4.2.3. Visualizing BNs.

4.3. Proposed approach -- 4.4. Experimental setup -- 4.5. Analysis and perspectives -- 4.6. Conclusion -- 5. Modeling Human Expertise Using Genetic Programming -- 5.1. Cooperative co-evolution -- 5.2. Modeling agrifood industrial processes -- 5.2.1. The Camembert cheese-ripening process -- 5.2.2. Modeling expertise on cheese ripening -- 5.3. Phase estimation using GP -- 5.3.1. Phase estimation using a classical GP -- 5.3.2. Phase estimation using a Parisian GP -- 5.3.3. Variable population size strategies in a Parisian GP -- 5.3.4. Analysis -- 5.4. Bayesian network structure learning using CCEAs -- 5.4.1. Recalling some probability notions -- 5.4.2. Bayesian networks -- 5.4.3. Evolution of an IM -- 5.4.4. Sharing -- 5.4.5. Immortal archive and embossing points -- 5.4.6. Description of the main parameters -- 5.4.7. BN structure estimation -- 5.4.8. Experiments and results -- 5.4.9. Analysis -- 5.5. Conclusion -- Conclusion -- Bibliography -- Index -- Other titles from iSTE in Computer Engineering -- EULA.