Leonardo César Corbalán

Evolving Neural Arrays A new mechanism for learning complex action sequences

Incremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially, starting from the simplest stage and thus increasing its generality and difficulty. The present work proposes neural array applications as a novel mechanism for complex actions sequence learning. Each array is composed by several neural nets obtained by means of an evolving process allowing them to acquire various degrees of specialization. Neural nets constituting the same array are organized so that, in each assessment, there is only one in charge of its response. The proposed strategy is applied to problems presented by obstacle evasion and target reaching as a means to show the capability of this proposal to solve complex problems. The measurements carried out show the superiority of evolving neural arrays over traditional neuroevolving methods that handle neural network populations ‐ SANE is being particularly used as a comparative reference due to its high performance. Neural array capability to recover from previous defective evolving stages has been tested, evincing highly plausible final successful outcomes ‐ even in those adverse cases. Finally, conclusions are presented as well as some future lines of work.

Multi-platform mobile application development analysis

Mobile devices have changed how we conceive software. There is a great range of development alternatives. In this paper, four different multi-platform development approaches (mobile web applications, hybrid, interpreted, and cross-compiled) are analyzed, and their most significant features through a case study are discussed.

Resources NeuroSearch in Peer-to-Peer networks

Unstructured Peer-to-Peer (P2P) networks as Gnutella are dynamic, distributed systems without any centralizing point favoring failure tolerance and strength. However, resource search in these systems is an important problem. Gnutellas breadth-first search algorithm is flooding-based and generates a large amount of traffic thus making scalability difficult. This paper proposes a new search algorithm in which nodes, assisted by their local neuronal networks, selectively send the query to the most appropriate subsets of neighbors only. Hence, Gnutella algorithm is significantly improved and provides a greater percentage of findings with less amount of traffic generated on P2P network.

Development frameworks for mobile devices: a comparative study about energy consumption

The development frameworks and the power efficiency in mobile devices have been studied separately. This paper deals with both topics together to determine how the selection of a development framework can impact on energy consumption of a mobile application. The focus is on applications with high processing load, audio and video playback. The results were analyzed and conclusions were reached.

GAAP. genetic algorithm with auxiliary populations applied to continuous optimization problems

Genetic algorithms have been used successfully to solve continuous optimization problems. However, an early convergence to low-quality solutions is one of the most common difficulties encountered when using these strategies. In this paper, a method that combines multiple auxiliary populations with the main population of the algorithm is proposed. The role of the auxiliary populations is dual: to prevent or hinder the early convergence to local suboptimal solutions, and to provide a local search mechanism for a greater exploitation of the most promising regions within the search space.