Fine-Tuning Restricted Boltzmann Machines Using No-Boundary Jellyfish

Abstract

Metaheuristic algorithms present elegant solutions to many problems regardless of their domain. The Jellyfish Search (JS) algorithm is inspired by how jellyfish searches for food in ocean currents and performs movements within the swarm. In this work, we propose a new version of the JS algorithm called No-Boundary Jellyfish Search (NBJS) to improve the convergence rate. The NBJS was applied to fine-tune a Restricted Boltzmann Machine (RBM) in the context of image reconstruction. For validating the proposal, the experiments were carried out on three public datasets to compare the performance of the NBJS algorithm with its original version and two other metaheuristic algorithms. The results showed that proposed approach is viable, for it obtained similar or even lower errors compared to models trained without fine-tuning.