Finding Strong Lottery Ticket Networks with Genetic Algorithms

According to the Strong Lottery Ticket Hypothesis, every sufficiently large neural network with randomly initialized weights contains a sub-network which - still with its random weights - already performs as well for a given task as the trained super-network. We present the first approach based on a genetic algorithm to find such strong lottery ticket sub-networks without training or otherwise computing any gradient. We show that, for smaller instances of binary classification tasks, our evolutionary approach even produces smaller and better-performing lottery ticket networks than the state-of-the-art approach using gradient information.
Comment: 12 pages, 7 figures, 5 tables, accepted for publication at the 16th International Joint Conference on Computational Intelligence (IJCCI 2024)