The “Logarithmic Weighted Random Selector” (LWRS) introduces a novel algorithm designed for selecting randomly one item from a list, with the bias that the further to the beginning of the list each one is, the more likely it is to be selected. Unlike traditional selection and sampling methods that rely on numerical fitness scores or require hyperparameter tuning, LWRS employs a logarithmic weighting mechanism to naturally favor items based on their position. It arose from the problem of requiring a method that allows weighting a selection based on the order of preference on a list of elements, for phenomena for which numerical weighting values ​​are unknown but only their order, which unlike algorithms such as Rank-based Selection, does not require configuration of hyperparameters. Different mechanisms were explored, such as a Linear Selection method and Exponential Selection. Discovering that utilizing a logarithmic scale, LWRS achieves a non-numerical preference bias, distinguishing itself from classic methods of weighted random sampling as Fitness-proportionate selection and Tournament Selection, which do require defined numerical weights, contributing to the field with a straightforward, parameter-free approach to weighted random selection.