Game Model Based Co-evolutionary Algorithm and Its Application for Multiobjective Nutrition Decision Making Optimization Problems

Wang, Gaoping; Bai, Li‐Yuan

doi:10.1007/978-3-540-74377-4_19

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…This implies that animal nutrition is in Pareto optimality, whereby any deviations on the nutrient intake or allocation to a trait w i would result in a cost to other traits w j [43]. Pareto optimality in nutritional decisions has been demonstrated in computer algorithms [44,45] but to our knowledge, has not yet been shown in empirical data. Currently, we rely on the growing use of GF to generate landscapes to test Pareto optimality on nutritional trade-offs and feeding behaviour.…”

Section: Discussionmentioning

confidence: 99%

Nutrigonometry III: curvature, area and differences between performance landscapes

Morimoto

Conceição

Smoczyk³

2022

R. Soc. open sci.

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Nutrition is one of the underlying factors necessary for the expression of life-histories and fitness across the tree of life. In recent decades, the geometric framework (GF) has become a powerful framework to obtain biological insights through the construction of multidimensional performance landscapes. However, to date, many properties of these multidimensional landscapes have remained inaccessible due to our lack of mathematical and statistical frameworks for GF analysis. This has limited our ability to understand, describe and estimate parameters which may contain useful biological information from GF multidimensional performance landscapes. Here, we propose a new model to investigate the curvature of GF multidimensional landscapes by calculating the parameters from differential geometry known as Gaussian and mean curvatures. We also estimate the surface area of multidimensional performance landscapes as a way to measure landscape deviations from flat. We applied the models to a landmark dataset in the field, where we also validate the assumptions required for the calculations of curvature. In particular, we showed that linear models perform as well as other models used in GF data, enabling landscapes to be approximated by quadratic polynomials. We then introduced the Hausdorff distance as a metric to compare the similarity of multidimensional landscapes.

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Section: Discussionmentioning

confidence: 99%

Nutrigonometry III: curvature, area and differences between performance landscapes

Morimoto

Conceição

Smoczyk³

2022

R. Soc. open sci.

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“…Computational models use relevant computing paradigms and formalisms such as: GA [22], Quantum Particle Swarm Optimisation [42], Ant Colony Optimisation [42,43] and Knapsack Problem [44]. Hybrid models integrate two or more models [29,37,45].…”

Section: Related Workmentioning

confidence: 99%

“…All these approaches propose schemes for managing incorporated knowledge that are tightly integrated with the model and hence not sufficient for the problem of this paper. They also do not assist in creating solutions that meet the definition of management of the incorporated knowledge as proposed in our previous paper [37].…”

Section: Related Workmentioning

confidence: 99%

Managing Knowledge in Computational Models for Global Food, Nutrition and Health Technologies

Dube¹,

McLachlan²,

Zanamwe³

et al. 2020

Preprint

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Computational models that need to incorporate domain knowledge for realistic solutions to problems often lead to technologies that get transferred to developing countries. The support for managing the knowledge incorporated into these technologies is important for customisation to suit local conditions. This work investigates this problem through the challenge of incorporating food and nutrition therapy guidelines (FNTG) into ICT-based solution models for the meal planning problem (MPP) for HIV/AIDS patients in developing countries. An experiment is undertaken to demonstrate the limitations of existing approaches and framework is investigated for manageable incorporation of knowledge into solution models. The paper contributes a clear understanding of, and directions for addressing, the problem of support for managing the knowledge incorporated into solutions models to support customisability of technologies. The significance of this contribution is that solutions will allow resulting technologies to be customised for use in different global contexts.

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Characterisation of Knowledge Incorporation into Solution Models for the Meal Planning Problem

Zanamwe

Dube

Thomson

et al. 2014

Foundations of Health Information Engineering and Systems

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Game Model Based Co-evolutionary Algorithm and Its Application for Multiobjective Nutrition Decision Making Optimization Problems

Cited by 4 publications

References 4 publications

Nutrigonometry III: curvature, area and differences between performance landscapes

Nutrigonometry III: curvature, area and differences between performance landscapes

Managing Knowledge in Computational Models for Global Food, Nutrition and Health Technologies

Characterisation of Knowledge Incorporation into Solution Models for the Meal Planning Problem

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