Wiley Evolutionary Algorithms for Food Science and Technology (Computer Engineering: Metaheuristics Set, 7)

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. Review Vol.:(0123456789Genetic Programming and Evolvable Machines (2019) 20:147–149https://doi.org/10.1007/s10710-018-9335-2Evelyne Lutton, Nathalie Perrot, Alberto Tonda:Evolutionary algorithms for food science and technologyWiley, 2016, 182 pp, ISBN: 978-1-119-13683-5Kelly Androutsopoulos1Published online: 30 August 2018© Springer Science+Business Media, LLC, part of Springer Nature 2018Lutton et al. show how to address some of the challenges related to optimization in the food science domain, by presenting ways to better integrate the role of the user in the optimization process. As stated in the book (preface, xxiii):“The user plays a key role in the optimization process: quality depends on the knowledge put into the design of the optimization task, and into the interpretation of the results.”The emphasis is on improving the quality of the solution, rather than just the speed or quantity, and if this leads to irresolution, this is deemed part of the process. This outlook is embodied in the two main aims of the book. Firstly, to show that adapting and customizing the evolutionary optimization algorithms to the specifics of the problem is a good strategy for improving quality. For example, Lutton et al. recommend using a cooperative co-evolutionary algorithm in which the fitness of an individual depends on its relationship to other members of the population. Secondly, to provide new ways to better integrate human expertise with evolutionary computation tools as certain quantities are very difficult to express using equations, e.g. taste and flavour. They proceed in making a convincing case that I agree with, that interactive evolutionary schemes are a rich ground for developing interactive modelling and decision-making in this domain.Evolutionary Algorithms for Food Science and Technology is well organized. The authors begin with a wonderful philosophical discussion in the preface, questioning the purpose of optimization and whether the right tools are used for addressing the right issues. It give a good motivation for the main aims in the book: why humans play an important role in the optimization process of real-world applications in food science, and that optimization algorithms should not be treated as “black boxes”. Instead we should allow for customization and fluid user interactions. E.g. by providing visualizations to aid interaction and by embedding assessments/judgements such as taste, flavour, perceptions, etc. The first chapter gives a good overview of the key features that make evolutionary computation challenging in food science. It also gives a panorama of the current uses of evolutionary optimization methods in this domain. This is particularly useful for readers that are new to the field of food science.The second chapter gives a clear and easy to understand introduction to evolutionary algorithms with lots of references to explore for a deeper understanding. The next three chapters describe three examples from the authors’ experience for some new usages of EA’s in food science. All successfully address one or the other of their two main aims (see above). Chapters 3, 4 and 5 can be read independently.Chapter three presents a methodology that combines EAs with visualisation to help food science experts explore in silico food models for enhancing their understanding. The structure of these models are intricate as they mir