A Software Tool for Assisting Experimentation in Dynamic Environments Novoa Hernández, Pavel Cruz Corona, Carlos Alberto Pelta Mochcovsky, David Alejandro Inteligencia artificial Artificial intelligence In real world, many optimization problems are dynamic, which means that their model elements vary with time. These problems have received increasing attention over time, especially from the viewpoint of metaheuristics methods. In this context, experimentation is a crucial task because of the stochastic nature of both algorithms and problems. Currently, there are several technologieswhose methods, problems, and performancemeasures can be implemented.However, in most of them, certain features thatmake the experimentation process easy are not present. Examples of such features are the statistical analysis of the results and a graphical user interface (GUI) that allows an easy management of the experimentation process. Bearing in mind these limitations, in the present work, we present DynOptLab, a software tool for experimental analysis in dynamic environments. DynOptLab has two main components: (1) an object-oriented framework to facilitate the implementation of new proposals and (2) a graphical user interface for the experiment management and the statistical analysis of the results. With the aim of verifying the benefits of DynOptLab’s main features, a typical case study on experimentation in dynamic environments was carried out. 2022-11-14T13:14:38Z 2022-11-14T13:14:38Z 2015-04-22 journal article Pavel Novoa-Hernández, Carlos Cruz Corona, David A. Pelta, "A Software Tool for Assisting Experimentation in Dynamic Environments", Applied Computational Intelligence and Soft Computing, vol. 2015, Article ID 302172, 12 pages, 2015. [https://doi.org/10.1155/2015/302172] https://hdl.handle.net/10481/77965 10.1155/2015/302172 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Hindawi