Co-evolution of symptom-herb relationship

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 coevolutionary 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