Interleaving algorithm for optimization of neural networks with self-learning perceptrons
Exploring the Efficiency of Interleaving Algorithms in Neural Network Optimization, this study introduces a novel application of team draft interleaving, diverging from traditional A/B testing methods. By simulating a sports team selection process, this approach enhances compound selection from a dataset. Highlighting its utility in artificial intelligence, particularly in self-learning perceptrons, the method enables perceptrons to adapt activation functions dynamically. This preemptive adjustment, facilitated by interleaving, marks a significant departure from conventional error backpropagation, demonstrating potential for more responsive learning mechanisms in neural networks.