A Software Tool for Assisting Experimentation in Dynamic Environments
Metadata
Show full item recordEditorial
Hindawi
Materia
Inteligencia artificial Artificial intelligence
Date
2015-04-22Referencia bibliográfica
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]
Sponsorship
Eureka SD project (Erasmus Mundus Action 2); Spanish Government TIN2011-27696-C02-01; Andalusian Government P11-TIC-8001; European Commission; University of Granada. GENIL-PYR-2014-9Abstract
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.