Manageability Challenges for Knowledge Incorporation in Genetic Algorithms: A Study Using the Meal Planning Problem

Ngonidzashe Zanamwe; Kudakwashe Dube; Jasmine Thomson

EAI International Conference for Research, Innovation and Development for Africa

Research Article

Manageability Challenges for Knowledge Incorporation in Genetic Algorithms: A Study Using the Meal Planning Problem

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@INPROCEEDINGS{10.4108/eai.20-6-2017.2270664,
    author={Ngonidzashe Zanamwe and Kudakwashe Dube and Jasmine Thomson},
    title={Manageability Challenges for Knowledge Incorporation in Genetic Algorithms: A Study Using the Meal Planning Problem},
    proceedings={EAI International Conference for Research, Innovation and Development for Africa},
    publisher={EAI},
    proceedings_a={ACRID},
    year={2018},
    month={4},
    keywords={meal planning manageability challenges knowledge incorporation knowledge formalisation modelling nutrition guideline knowledge engineering algorithms optimisation problem genetic algorithm nutrition therapy meal planning problem menu planning},
    doi={10.4108/eai.20-6-2017.2270664}
}

Ngonidzashe Zanamwe
Kudakwashe Dube
Jasmine Thomson
Year: 2018
Manageability Challenges for Knowledge Incorporation in Genetic Algorithms: A Study Using the Meal Planning Problem
ACRID
EAI
DOI: 10.4108/eai.20-6-2017.2270664

Ngonidzashe Zanamwe¹^,*, Kudakwashe Dube², Jasmine Thomson³

1: University of Zimbabwe, Computer Science Department
2: School of Engineering and Advanced Technology, College of Sciences, Massey University, New Zealand
3: Institute of Food Science and Technology, College of Health, Massey University, New Zealand

*Contact email: nbzanamwe@gmail.com

Abstract

This work investigates manageability challenges for knowledge incorporation in Genetic Algorithms (GAs) for solving the Meal Planning Problem (MPP). The MPP is an intractable problem with optimal solution models in literature leading to solutions that are hard to manage from the knowledge perspective. Manageable incorporation of knowledge into computational models for the MPP will help dietitians in nutrition-based disease therapy administration thereby improving the health of patients. An experimental study implementing a genetic model for the MPP was used to investigate the manageability challenges. The findings were that GAs do not have natural ways of supporting manageable incorporation of knowledge and knowledge incorporated into GAs using existing methods is hard to manage. Manageability is important because knowledge and models become easy to customize to suit different contexts. The novel contribution of this work is a new understanding on the matter of “manageability”.

Keywords: meal planning manageability challenges knowledge incorporation knowledge formalisation modelling nutrition guideline knowledge engineering algorithms optimisation problem genetic algorithm nutrition therapy meal planning problem menu planning

Published: 2018-04-06
Publisher: EAI

: http://dx.doi.org/10.4108/eai.20-6-2017.2270664