Modeling Performance of NP-hard Problems by Applying Causal Analysis for the VisTHAA Tool

At present the analysis of the algorithms is a necessary process, especially algorithms that solve difficult problems in the daily life; Since the analysis of algorithms helps to give explanations of the performance and to understand the behavior of the algorithms when the input data change it means, the input instances present inherent characteristics of the environment of the problem to solve, causing a differently behave in the algorithms. In the scientific literature, several researchers have been given the task of working in this area of research, for example, The Multi-objective Optimization and The Analysis and Design of Optimization Algorithms Via Integral Quadratic Constraints which addresses the analysis of algorithms for optimization problems. This research work proposes the incorporation of a set of modules that allow researchers to redesign algorithms that solve NP-Hard problems belonging to the container family through a causal methodology, allowing a formal explanation of the behavior of algorithms through indexes of problem structure, behavior, trajectory, and algorithmic performance. These modules are incorporated into an analysis tool called VisTHAA.