Швай, НадіяКотляренко, Анастасiя2025-09-032025-09-032025https://ekmair.ukma.edu.ua/handle/123456789/36404This work studies neural network pruning with metaheuristic optimization methods. Pruning was formulated as an optimization problem with a target function that is a weighted sum of neural network accuracy and sparsity. This problem was solved with stochastic metaheuristic methods (Genetic Algorithm and Particle Swarm Optimization) that generate binary masks. Obtained results demonstrate that pruning with metaheuristic methods is comparative with 𝐿2 pruning when finetuning is possible and is significantly more performant when no post-pruning finetuning is available.en-USneural networkspruningmetaheuristicGenetic AlgorithmParticle Swarm Optimization𝐿2 pruningbachelor`s thesisOther