Transfer learning with long term artificial neural network memory (LTANN-MEM) and neural symbolization algorithm (NSA) for solving high dimensional multi-objective symbolic regression problems
Amr K. Deklel, Mohammed A. Saleh, Alaa Mahmoud Hamdy, El-Sayed Mostafa Saad · 2017
Long Term Artificial Neural Network Memory (LTANN-MEM) and Neural Symbolization Algorithm (NSA) are proposed for solving symbolic regression problems. Although this approach is capable of solving Boolean decoder problems of sizes 6, 11 and 20, it is not capable of solving decoder problems of higher dimensions like decoder-37; decoder-n is decoder with sum of inputs and outputs is n for example decoder-20 is decoder with 4 inputs and 16 outputs. It is shown here that LTANN-MEM and NSA approach is a kind of transfer learning however it lacks for sub tasking transfer and updatable LTANN-MEM. An approach for adding the sub tasking transfer and LTANN-MEM updates is discussed here and examined by solving decoder problems of sizes 37, 70 and 135 efficiently. Comparisons with two learning classifier systems are performed and it is found that the proposed approach in this work outperforms both of them. It is shown that the proposed approach is used also for solving decoder-264 efficiently. According to the best of our knowledge, there is no reported approach for solving this high dimensional problem.