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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.

'Industrial applications of evolutionary algorithms'is intended as a resource for both experienced users of evolutionary algorithms and researchers that are beginning to approach these fascinating optimization techniques.Experienced users will find interesting details of real-world problems, advice on solving issues related to fitness computation or modeling, and suggestions on how to set the appropriate parameters to reach optimal solutions.Beginners will find a thorough introduction to evolutionary computation, and a complete presentation of several classes of evolutionary algorithms exploited to solve different problems. Inside, scholars will find useful examples on how to fill the gap between purely theoretical examples and industrial problems.The collection of case studies presented is also extremely appealing for anyone interested in Evolutionary Computation, but without direct access to extensive technical literature on the subject.After the introduction, each chapter in the book presents a test case, and is organized so that it can be read independently from the rest: all the information needed to understand the problem and the approach is reported in each part. Chapters are grouped by three themes of particular interest for real-world applications, namely prototype-based validation, reliability and test generation.The authors hope that this volume will help to expose the flexibility and efficiency of evolutionary techniques, encouraging more companies to adopt them; and that, most of all, you will enjoy your reading.