Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21554
Title: Co-evolutionary genetic algorithm in symptom-herb relationship discovery
Authors: Poon, J
Yin, D
Poon, SK
Zhou, X
Zhang, R
Liu, B
Sze, D
Keywords: Co-evolution
Genetic Algorithm
Symptom-Herb relationship
Interactions
Issue Date: 2011
Publisher: IEEE
Source: 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 12-15 November 2011, Atlanta, GA, p. 890-894 How to cite?
Abstract: Traditional Chinese Medicine (TCM) is a holistic approach to medical treatment. The symptoms from a diagnosis are grouped into overlapping sets of symptoms, where each set of symptoms may demand the use of a different set of herbs. Since there are multiple mappings between symptoms and herbs, the discovery of the symptoms-herbs relationship is a crucial step to the research of the underlying TCM principle. The discovery of many existing formulas took a long time to stabilize to the current configurations. In this paper, the relationship discovery is argued to be more than just an evolutionary process, but a co-evolutionary process, i.e. a set of symptoms searches for candidate sets of herbs, while a given set of herbs also searches for multiple sets of symptoms that it can be applied. In other words, a well recognized symptoms-herbs relationship is the result of a dynamic equilibrium of two inter-related evolutionary processes. This model of discovery was implemented using a Combined Gene Genetic Algorithm (CoGA1) where the symptoms and herbs are encoded in the same chromosome to evolve over time. The algorithm was tested with an insomnia dataset from a TCM hospital. The algorithm was able to find the symptoms-herbs relationships that are consistent with TCM principles.
URI: http://hdl.handle.net/10397/21554
ISBN: 978-1-4577-1612-6
DOI: 10.1109/BIBMW.2011.6112492
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