Another Masked Language Model (MLM) by was trained to serve as a random mutation operator, aiming to generate new optimized molecules. Two generation strategies were applied and compared; fixed and adaptive, the fixed strategy refers to the fixed pre-trained set of molecules while the adaptive strategy updates and learns from the newly generated mutated molecules in each iteration. Starting with a simpler task, these strategies were tested on molecular generation; the adaptive strategy produced mutated molecules with closer features of synthesizability and drug-likeness to the first generation while the fixed strategy was biased and produced mutations prevalent in the pre-trained set. However, there were not any significant differences in the generation time both strategies needed. In the case of molecule optimization, two opposite scenarios are possible; depending on the quality of the initial data. When had poor data, the adaptive strategy failed and generated less valid and less acceptable molecules due to continuously learning from molecules with poor scores. On the other hand, when we had highscore data, the adaptive strategy outperformed the fixed strategy and generated more valid and acceptable molecules in all aspects. After testing, the optimal strategy was to start with five fixed-generations followed by twenty adaptive-generations.