There are not many tools in the evolutionary computing field that allow researchers to implement, modify or compare different algorithms. Additionally, those tools usually lack flexibility, maintenance or some other characteristic, so researchers program their own solutions most of the time, reimplementing algorithms that have already been implemented hundreds of times. This paper introduces a new framework for evolutionary computation called JEAF (Java Evolutionary Algorithm Framework) that tries to offer a platform to facilitate the tasks of comparing, analyzing, modifying and implementing evolutionary algorithms, reusing components and programming as few as possible. JEAF also aims to be a tool for evolutionary algorithm users that employ these algorithms to solve other problems not related with evolutionary computation. In this sense, JEAF provides methods to distribute an evolutionary process and to plug external tools to perform the evaluation of candidate solutions.

Human interaction environments (HIE) must be understood as any place where people carry out their daily life, including their work, family life, leisure and social life, interacting with technology to enhance or facilitate the experience. The integration of technology in these environments has been achieved in a disorderly and incompatible way, with devices operating in isolated islands with artificial edges delimited by the manufacturers. In this paper we are presenting the UniDA framework, an integral solution for the development of systems that require the integration and interoperation of devices and technologies in HIEs. It provides developers and installers with a uniform conceptual framework capable of modelling an HIE, together with a set of libraries, tools and devices to build distributed instrumentation networks with support for transparent integration of other technologies. A series of use case examples and a comparison to many of the existing technologies in the field has been included in order to show the benefits of using UniDA.

UAVs often perform tasks that require flying close to walls or structures and in environments where a satellite-based location is not possible. Flying close to solid bodies implies a higher risk of collisions, thus requiring an increase in the precision of the measurement and control of the UAV’s position. The aerodynamic distortions generated by nearby walls or other objects are also relevant, making the control more complex and further placing demands on the positioning system. Performing wall-related tasks implies flying very close to the wall and, in some cases, even touching it. This work presents a Near-Wall Positioning System (NWPS) based on the combination of an Ultra-wideband (UWB) solution and LIDAR-based range finders. This NWPS has been developed and tested to allow precise positioning and orientation of a multirotor UAV relative to a wall when performing tasks near it. Specific position and orientation control hardware based on horizontal thrusters has also been designed, allowing the UAV to move smoothly and safely near walls.

The objective of this paper is to report on new developments in the project we are working on for the development of a mobile sensor based opportunistic urban pollution monitoring network. This work follows from the implementation of a single pollution sensor based sensing node prototype which was used for testing an opportunistic communications network and which was reported elsewhere. Here we concentrate on the extension of the basic sensing system and its modular conversion into a multi pollutant sensing system able to additionally provide temperature, humidity and geo-position information as well as on the software architecture developed around it in order to process the huge amounts of data the system produces. The different prototypes were tested on the public transportation system of the city of Vigo and on multiple test runs around the city of A Coruña in the north-west of Spain producing very promising results.

This paper discusses an algorithm that provides a way to obtain ensembles of collaborating artificial neural networks (ANNs) online. That is, its purpose is to find solutions to problems based on the interaction of sets of, in principle, heterogeneous ANNs whose joint behaviour results in an emergent solution. This approach is intrinsically able to handle lifelong adaptation within the society in order to comply with changing situations or demands in dynamic environments. It is called Asynchronous Situated Coevolution (ASiCo) and was designed for the lifelong coevolution of artificial neural network societies. ASiCo deals with the evolutionary part of neuroevolution and it can support any type of neural network structure or even neural network construction mechanism. Consequently, it can be extended with some of the techniques found in single ANN neuroevolutionary mechanisms when considering the simultaneous evolution of network weights and network topology. The operation and characteristics of this strategy are illustrated through some experiments carried out using a well known benchmark collaboration task.