Optimizing algorithmic models demands precision and stability in parameter selection. The complexity increases with the integration of strict parameters from proprietary firms. Developing a Custom Criterion allows for targeted optimization without extensive manual analysis. However, caution is needed to avoid issues like the misuse of return(0) in optimization processes that could lead to discarding viable results. Adapting principles from Neural Networks, such as Activation Functions, can refine parameter selection by offering structured ways to handle data ranges and improve scoring methods. Functions like Sigmoid and Tanh are particularly beneficial due to their constrained and stable output ranges, preventing issues like exploding or vanishing gradients. This approach advances the capability to harness genetics-based algorithms for superior optimizatio... #MQL5 #MT5 #AI #Algorithm https://lnkd.in/db_R_Vjh
