The combination of Quality of Thing (QoT) with Internet of Things (IoT) systems can be challenging because of the vast number of connected devices, diverse types of applications and services, and varying network conditions. During the process of composing these Things, heterogeneity arises as an uncertainty. Hence, uncertainty and imprecision emerge as a consequence of the plethora of things as well as the variety of the composition paths. One way to address these challenges is through the use of fuzzy logic to mimic uncertainty and imprecision modeling and genetic algorithm to find the optimal path. As a result, we propose a model for the Thing behaviour based on QoT non-functional properties. As well as we propose a hybrid approach for modeling the uncertainty of the configurable composition based on fuzzy logic and genetic algorithm. Our approach helps to ensure that IoT applications and services receive the resources they need to function effectively, even in the presence of varying network conditions and changing demands.