Stable online evolutionary learning of NN-MLP
Qiangfu Zhao · IEEE Transactions on Neural Networks · 1997
To design the nearest-neighbor-based multilayer perceptron (NN-MLP) efficiently, the author has proposed a nongenetic-based evolutionary algorithm called the R(4)-rule. For off-line learning, the R(4)-rule can produce the smallest or nearly smallest networks with high generalization ability by iteratively performing four basic operations: recognition, remembrance, reduction, add review. This algorithm, however, cannot be applied directly to online learning because its inherent instability, which is caused by over-reduction and over-review. To stabilize the R(4)-rule, this paper proposes some improvements for reduction and review. The improved reduction is more robust for online learning because the fitness of each hidden neuron is defined by its overall behavior in many learning cycles. The new review is more efficient because hidden neurons are adjusted in a more careful way. The performance of the improved R (4)-rule for online learning is shown by experimental results.