Brain Storm Optimization (BSO) is a population-based algorithm developed based on the humans brainstorming process. It has been successfully applied to many applications in the domain of non-linear continuous optimization. The performance of BSO has been enhanced in the literature through many works attempting to improve its different stages. In this work, we propose a Gaussian Bare-Bones version of the Global-best BSO algorithm (BBGBSO). The idea of bare-bones implementations in general is inspired from the convergence characteristics of Particle Swarm Optimization (PSO) where particles converge to the weighted average of the personal-best of the particle and the global-best of the swarm. A number of previous Bare-bones implementations have been proposed in the literature for different algorithms resulting in noticeable performance improvements. Experimental results extracted from many benchmark functions across different problem sizes confirms the promising performance of BBGBSO.