How to make a best-seller: optimal product design problems

We formalize and analyze the computational complexity of three problems which are at the keystone of anymarketingmanagement process. Given the preferences of customers over the attribute values wemay assign to our product (i.e. its possible features), the attribute values of products sold by our competitors, and which attribute values are available to each producer (due to e.g. technological limitations, legal issues, or availability of resources), we consider the following problems: (a) select the attributes of our product in such a way that the number of customers is maximized; (b) find out whether there is a feasible strategy guaranteeing that, at some point in the future before some deadline, we will reach a given average number of customers during some period of time; and (c) the same question as (b), though the number of steps before the deadline is restricted to be, at most, the number of attributes. We prove that these problems are Poly-APX-complete, EXPTIME-complete, and PSPACE-complete, respectively. After presenting these theoretical properties, heuristic methods based on genetic, swarm and minimax algorithms are proposed to suboptimally solve these problems. We report experimental results where these methods are applied to solve some artificially-designed problem instances, and next we present a case study, based on real data, where these algorithms are applied to a particular kind of product: we automatically design the political platform of a political party to maximize its numbers of votes in an election (problem (a)) and its number of supporters along time (problems (b) and (c)). The problem instances solved in this case study are constructed from publicly released polls on political tendencies in Spain

Applications of river formation dynamics

River formation dynamics is a metaheuristic where solutions are constructed by iteratively modifying the values associated to the nodes of a graph. Its gradient orientation provides interesting features such as the fast reinforcement of new shortcuts, the natural avoidance of cycles, and the focused elimination of blind alleys. Since the method was firstly proposed in 2007, several research groups have applied it to a wide variety of application domains, such as telecommunications, software testing, industrial manufacturing processes, or navigation. In this paper we review the main works of the last decade where the river formation dynamics metaheuristic has been applied to solve optimization problems

Herramienta para escoger sistemáticamente combinaciones de ejercicios con mayor capacidad formadora y/o evaluadora

Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaFALSEsubmitte

Inteligencia colectiva aplicada al aula: Realimentación docente a través de preguntas con dificultades específicas diseñadas por alumnos

Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaFALSEsubmitte

Parallelizing Particle Swarm Optimization in a Functional Programming Environment

Many bioinspired methods are based on using several simple entities which search for a reasonable solution (somehow) independently. This is the case of Particle Swarm Optimization (PSO), where many simple particles search for the optimum solution by using both their local information and the information of the best solution found so far by any of the other particles. Particles are partially independent, and we can take advantage of this fact to parallelize PSO programs. Unfortunately, providing good parallel implementations for each specific PSO program can be tricky and time-consuming for the programmer. In this paper we introduce several parallel functional skeletons which, given a sequential PSO implementation, automatically provide the corresponding parallel implementations of it. We use these skeletons and report some experimental results. We observe that, despite the low effort required by programmers to use these skeletons, empirical results show that skeletons reach reasonable speedups

Desarrollo de una aplicación (App) para plataformas móviles para mejorar la enseñanza/aprendizaje de sistemas de numeración y operaciones elementales en la formación de maestros (PIMCD-2015-216)

Memoria del proyecto de innovación y mejora de la calidad docente 126 de la convocatoria de 2015 de la Universidad Complutense de Madrid. Se desarrolls una aplicación (App) para plataformas móviles para mejorar la enseñanza/aprendizaje de sistemas de numeración y operaciones elementales en la formación de maestros haciendo uso de sistemas de numeración análogos a los de uso convencional y algoritmos de las cuatro operaciones aritméticas elementales en dichos sistemas

A Cognitive-based Tool to Teach how to Teach

Depto. de Sistemas Informáticos y ComputaciónDepto. de Didáctica de las Ciencias Experimentales , Sociales y MatemáticasFac. de InformáticaFac. de EducaciónTRUEpu