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The purpose of this paper is to apply a multi‐objective Bayesian artificial immune system (MOBAIS) to feature selection in classification problems aiming at minimizing both the classification error and cardinality of the subset of features. The algorithm is able to perform a multimodal search maintaining population diversity and controlling automatically the population size according to the problem. In addition, it is capable of identifying and preserving building blocks (partial components of the whole solution) effectively.

The algorithm evolves candidate subsets of features by replacing the traditional mutation operator in immune‐inspired algorithms with a probabilistic model which represents the probability distribution of the promising solutions found so far. Then, the probabilistic model is used to generate new individuals. A Bayesian network is adopted as the probabilistic model due to its capability of capturing expressive interactions among the variables of the problem. In order to evaluate the proposal, it was applied to ten datasets and the results compared with those generated by state‐of‐the‐art algorithms.

The experiments demonstrate the effectiveness of the multi‐objective approach to feature selection. The algorithm found parsimonious subsets of features and the classifiers produced a significant improvement in the accuracy. In addition, the maintenance of building blocks avoids the disruption of partial solutions, leading to a quick convergence.

The originality of this paper relies on the proposal of a novel algorithm to multi‐objective feature selection.

The purpose of this paper is to present an artificial homeostatic system whose parameters are defined by means of an evolutionary process. The objective is to design a more biologically plausible system inspired by homeostatic regulations observed in nature, which is capable of exploring key issues in the context of robot behaviour adaptation and coordination.

The proposed system consists of an artificial endocrine system that coordinates two spatially unconstrained GasNet artificial neural network models, called non‐spatial GasNets. Both systems are dedicated to the definition of control actions in autonomous navigation tasks via the use of an artificial hormone and a hormone receptor. A series of experiments are performed in a real and simulated scenario in order to investigate the performance of the system and its robustness to novel environmental conditions and internal sensory disruptions.

The designed system shows to be robust enough to self‐adapt to a wider variety of disruptions and novel environments by making full use of its in‐built homeostatic mechanisms. The system is also successfully tested on a real robot, indicating the viability of the proposed method for coping with the reality gap, a well‐known issue for the evolutionary robotics community.

The proposed framework is inspired by the homeostatic regulations and gaseous neuro‐modulation that are intrinsic to the human body. The incorporation of an artificial hormone receptor stands for the novelty of this paper. This hormone receptor proves to be vital to control the network's response to the signalling promoted by the presence of the artificial hormone. It is envisaged that the proposed framework is a step forward in the design of a generic model for coordinating many and more complex behaviours in simulated and real robots, employing multiple hormones and potentially coping with further severe disruptions.

In the past months, the authors have experienced an exceptional global situation that especially affects the most vulnerable population. This paper aims to analyse the needs, strengths and good practices of the organisations that have continued to study with the migrant population during the health crisis. The main objective was to determine how the health, social and communication crisis has affected the management of the organisation itself, the communications with its direct beneficiaries, the communications with the rest of society, as well as the perception that organisations specialised in migration have about how media has communicated the information of COVID-19 and migrant population.

The authors have circulated a questionnaire with open questions that covered the four dimensions previously mentioned.

The results show the analysis of the answers of 11 of the most important national and international organisations in the field of migration and refuge that operate in Spain.

Key issues have emerged related not only to the principal management concerns, internal digital communication, the adaptability of external communication and the major effort required to provide information about migration but also to innovative good practices. That other third sector organisations focussed on migration will be able to apply in the future and in other geographic areas.