Investigating Innovized Progress Operators with Different ML Methods

Chapters 5 and 6 have shown how learning efficient search directions from the intermittent generations’ solutions could be utilized to create pro-convergence and pro-diversity offspring, enabling better convergence and diversity, respectively. The entailing steps of dataset preparation, training of ML models, and utilization of these models have been encapsulated as Innovized Progress operators, namely IP2 for convergence improvement and IP3 for diversity improvement. In these chapters, the goal was to establish that ML-based operators can potentially enhance the performance of RV-EMâOAs. In doing so, major emphasis was laid on the design of these operators adhering to the key considerations of convergence–diversity balance and ML risk–reward trade-off, and avoiding ad hoc parameter fixations and extra solution evaluations. Noticeably, the impact of the choice of the specific ML methods used in these operators was not discussed. However, to endorse the robustness of the proposed (IP2, IP3, and UIP) operators, it is imperative to investigate how significantly their performance can be influenced when the underlying ML methods are varied.