Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11584
Title: Co-evolution of symptom-herb relationship
Authors: Poon, J
Yin, D
Poon, SK
Zhang, R
Liu, B
Sze, D
Keywords: Co-evolution
Genetic Algorithm
Symptom-Herb relationship
Traditional Chinese Medicine
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE Congress on Evolutionary Computation (CEC), 10-15 June 2012, Brisbane, QLD, p. 1-6 How to cite?
Abstract: Traditional Chinese Medicine (TCM) is a complementary alternative medical approach. Its holistic approach is drastically different from the western medicine (WM). Upon the gathering of various symptoms in a diagnosis, a TCM practitioner prescribes treatment methods, of which herbal medicine is still one of the most popular. Each formula consists of multiple herbs. Since it is not a one-to-one mapping between symptom and herb, overlapping subsets of herbs are meant to address sets of overlapping symptoms. As a result, 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 are appropriate for multiple sets of symptoms. 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 and have better fitness from Simple GA.
URI: http://hdl.handle.net/10397/11584
ISBN: 978-1-4673-1510-4
978-1-4673-1508-1 (E-ISBN)
DOI: 10.1109/CEC.2012.6256575
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