TY  - BOOK
AU  - Lutton,Evelyne
AU  - Perrot,Nathalie
AU  - Tonda,Alberto
TI  - Evolutionary algorithms for food science and technology
T2  - Metaheuristics set
SN  - 9781119136828
AV  - TP370.5 
U1  - 664.001/5118 23
PY  - 2016///
CY  - London, UK
PB  - ISTE, Ltd.
KW  - Food industry and trade
KW  - Mathematical models
KW  - Evolutionary computation
KW  - COMPUTERS / Computer Engineering
KW  - bisacsh
KW  - fast
KW  - TECHNOLOGY & ENGINEERING / Food Science
KW  - Electronic books
N1  - Includes bibliographical references and index
N2  - 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
UR  - https://doi.org/10.1002/9781119136828
ER  -