Sammendrag
In this thesis, we introduced two new applications of Machine Learning in the field of Economic Optimization. The first application addresses the problem of searching for global Self-Optimizing variables. We applied Genetic Programming (GP) to solve this problem and demonstrated how powerful is the new GP-based search method. In the second application, we used Convolutional Neural Networks (CNN) to develop a vision-based steady-state detector (SSD) for steady-state Real-Time Optimizers. It was our purpose to investigate if this vision-based SSD has higher accuracy than established statistical SSD. We found that they have comparable performances, but the CNN-based detector possesses certain advantages that the others do not have.