Components of 29 wild type tea single plants collected from Dayao Mountain, in Guangxi province, South China, were investigated. They included tea polyphenols, free amino acids, catechin, amino acid and alkaloid monomers etc. Genetic diversity and clustering analyses were conducted based on the main biochemical components. Meanwhile, genetic relationships among 6 wild type tea plants representing 3 tea populations of Daoyao Mountain with 15 tea varieties grown in Yunnan, Guangdong, Hunan, Fujian provinces were analyzed by random amplified polymorphic DNA. The results showed that wild type tea plants from Dayao Mountain were of rich genetic diversity. Furthermore, some tea germplasms with high quality, including high contents of amino acids, high epigallocatechin gallate, and high caffeine have been discovered. These wild type tea germplasms are of high values for further development values due to their geographical uniqueness.
In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal criteria for phylogenies, various kinds of multi-objective metaheuristics have been used to reconstruct phylogenies. However, combining these two time-consuming methods results in those multi-objective metaheuristics being slower than a single objective. Therefore, we propose a novel, multi-objective optimization algorithm, MOEA-RC, to accelerate the processes of rebuilding phylogenies using structural information of elites in current populations. We compare MOEA-RC with two representative multi-objective algorithms, MOEA/D and NAGA-II, and a non-consensus version of MOEA-RC on three real-world datasets. The result is, within a given number of iterations, MOEA-RC achieves better solutions than the other algorithms.
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