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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.


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The structure of these models are intricate as they mirror the complex phenomena involved in these real-world processes. When exploring the models, one of the things that experts find hard is to find meaningful correlations between variables. The approach was tested on a specific model of milk gel structures.

Evolutionary Algorithms - Objective Functions

The formation of milk gels is the first step in both cheese and yoghurt manufacture. One of the main research lines on milk gel is devoted to the development of models with the ability to replicate the dynamics of gel formation at relevant scales, linking the structure to macroscopic properties. As a non-expert in food sciences I found this model difficult to understand, but the authors provide a useful glossary of variables for reference, plenty of citations and lots of insights into the process that are useful to understand how to use this approach to explore other models.

The exploration of the model is done by visualizing the data collected during the execution of an EA using a ultidimensional visualization tool called GraphDice. A reader would find the description of how to use GraphDice in this way useful for replicating the process. The exploration resulted in experts finding a correlation between two parameters, previously considered independent.

A Bayesian network is a probabilistic directed acyclic graph whereby the nodes represent variables and the edges represent conditional dependencies between the variables. Learning the optimal structure of a Bayesian network is an NP-hard problem and even finding good approximations is extremely hard.

This is because a balance between the complexity and representiveness of the model must be found. In chapter four, a preliminary study was conducted to explore what is the best trade off between automatic evolution and user interaction for finding possible solutions for the problem of learning Bayesian network structures.

The authors developed a prototype tool with a graphical-user interface that allows a domain expert user to guide the evolution of a network by alternating between automatic and fully interactive steps. Their approach was tested with two experts: one analyzing a dataset on cheese ripening and another a dataset on biscuit baking. The feedback given by the experts helped Lutton et al. This list is noteworthy for any readers that want to adopt this approach. Chapter five is the longest and presents in great technical depth two approaches for dealing with modelling issues based on cooperative co-evolution schemes.

The experiments focused on the modelling of a Camembert cheese ripening process. The first approach explores how genetic programming GP and cooperative-co-evolution algorithms can be used to learn expert knowledge. While the second addresses the problem of learning the structure of a Bayesian network, with an approach based on independent models.

Evolutionary Algorithms for Food Science and Technology - eBook - ipixaradugit.tk

In all three technical chapters, the authors articulate well key issues and insights for each approach. Such knowledge only comes from experience. There are also plenty of useful tables and figures illustrating results. Some of the figures in the book are difficult to read because they are in grayscale rather than colour.

A genetic algorithm predicts the vertical growth of cities

The authors have provided URLs to colour versions of the figures, however, these are broken and do not resolve to content. This operation is repeated again and again until the algorithms get the most accurate results. The study, published in the Journal of Urban Planning and Development , has focused on one of the neighbourhoods with the highest vertical growth in the world in recent years: the Minato Ward, in Tokyo, where the headquarters of multinational companies such as Mitsubishi, Honda, NEC, Toshiba or Sony, are located.

In , once all the information had been gathered, the authors created a series of maps and 3D representations of Minato to be able to predict the number of buildings and their probable locations within this booming ward in the following years during the period. According to the authors, the algorithm not only estimates the number of future skyscrapers in a neighbourhood of the city, but also the specific areas where they will be most likely be located.

Note: Content may be edited for style and length. Science News. ScienceDaily, 25 May A genetic algorithm predicts the vertical growth of cities. Retrieved September 24, from www. This data could eventually provide a basis to help improve our Although genetic differences are minor, they may influence how well the insects adapt to their habitat.