Automated　Model　Generation　(AMG)　algorithms　are　an　important　technique　for　creating　Artificial　Neural　Network　(ANN)　models　in　microwave　design　automation.　AMG　integrates　all　the　subtasks　in　ANN　development　into　a　unified　automated　algorithm.　The　assessments　of　the　ANN　training　phenomena　related　to　under-learning,　over-learning　and　good-learning　are　automated　and　the　quantitative　links　between　the　accuracy　of　the　ANN　model,　the　amount/distribution　of　training/testing　data,　and　the　size　of　the　neural　network　are　established.　In　this　way,　the　AMG　algorithm　automatically　creates　an　ANN　model　with　user-desired　accuracy,　significantly　reducing　the　human　time　required　for　modeling.　This　paper　introduces　the　state　of　the　art　in　AMG　algorithms　and　its　applications　in　microwave　modeling.