Abstract：
Marine macro-algae, commonly known as \"seaweed,\" are used in everyday commodity products worldwide for food, feed, and biostimulant for plants and animals and continue to be one of the conspicuous components of world aquaculture production. However, the application of ANN in seaweeds remains limited. Here, we described how to perform ANN-based machine learning modeling and GA-based optimization to enhance seedling production for implications on commercial farming. The critical steps from seaweed seedling explant preparation, selection of independent variables for laboratory culture, formulating experimental design, executing ANN Modelling, and implementing optimization algorithm are described.
摘要：
海洋大型藻类，俗称“海藻，“在世界各地的日常商品中用于食品，饲料，和植物和动物的生物刺激剂，并继续成为世界水产养殖生产的显着组成部分之一。然而,人工神经网络在海藻中的应用仍然有限。这里,我们描述了如何执行基于人工神经网络的机器学习建模和基于GA的优化，以提高幼苗产量，从而对商业化农业产生影响。从海藻幼苗外植体制备的关键步骤,选择实验室培养的独立变量，制定实验设计，执行人工神经网络建模，并对优化算法的实现进行了描述。
