Premature convergence is one of the best-known drawbacks that affects the performance of evolutionary algorithms. An alternative for dealing with this problem is to explicitly try to maintain proper diversity. In this paper, a new replacement strategy that preserves useful diversity is presented. The novelty of our method is that it combines the idea of transforming a single-objective problem into a multiobjective one, by considering diversity as an explicit objective, with the idea of adapting the balance induced between exploration and exploitation to the various optimization stages. Specifically, in the initial phases, larger amounts of diversity are accepted. The diversity measure considered in this paper is based on calculating distances to the closest surviving individual. Analyses with a multimodal function better justify the design decisions and provide greater insight into the working operation of the proposal. Computational results with a packing problem that was proposed in a popular contest illustrate the usefulness of the proposal. The new method significantly improves on the best results known to date for this problem and compares favorably against a large number of state-of-the-art schemes.

Contribution: This document presents a systematic bibliographic review that demonstrates the need to conduct research on how the user experience impacts the development of computational thinking. Background: In the field of computer science, computational thinking is defined as a method that enhances problem-solving skills, system design, and human behavior understanding. Over the last few decades, several tools have been proposed for the development of computational thinking skills; however, there is no area of study that evaluates the implications or the impact that these types of platforms have on users belonging to any knowledge area. Research Question: Do user interfaces influence the development of computational thinking skills? Methodology: To address this issue, a systematic review of the literature was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology for analyzing and evaluating scientific publications. Findings: The results show that despite the dearth of literature on the subject, the specific design of a user interface has a significant impact on the development of computational thinking. Bearing the above in mind, it is necessary to conduct research that delves more deeply into the effects caused by the technologies that are used to develop computational thinking, this being a line of research that is worthy of consideration.

One of the main components of most modern Multi-Objective Evolutionary Algorithms (MOEAs) is to maintain a proper diversity within a population in order to avoid the premature convergence problem. Due to this implicit feature that most MOEAs share, their application for Single-Objective Optimization (SO) might be helpful, and provides a promising field of research. Some common approaches to this topic are based on adding extra—and generally artificial—objectives to the problem formulation. However, when applying MOEAs to implicit Multi-Objective Optimization Problems (MOPs), it is not common to analyze how effective said approaches are in relation to optimizing each objective separately. In this paper, we present a comparative study between MOEAs and Single-Objective Evolutionary Algorithms (SOEAs) when optimizing every objective in a MOP, considering here the bi-objective case. For the study, we focus on two well-known and widely studied optimization problems: the Knapsack Problem (KNP) and the Travelling Salesman Problem (TSP). The experimental study considers three MOEAs and two SOEAs. Each SOEA is applied independently for each optimization objective, such that the optimized values obtained for each objective can be compared to the multi-objective solutions achieved by the MOEAs. MOEAs, however, allow optimizing two objectives at once, since the resulting Pareto fronts can be used to analyze the endpoints, i.e., the point optimizing objective 1 and the point optimizing objective 2. The experimental results show that, although MOEAs have to deal with several objectives simultaneously, they can compete with SOEAs, especially when dealing with strongly correlated or large instances.

SCHOOLTHY: Automatic Menu Planner for Healthy and Balanced School Meals is a decision support tool that addresses the multi-objective menu planning problem in order to automatically produce meal plans for school canteens. Malnutrition is a widespread problem nowadays and is particularly serious when it affects children. In our environment, nutrition experts design healthy and balanced meal plans for children manually, which leaves significant room for improvement in terms of convenience and efficiency. SCHOOLTHY is presented herein as a proposal to improve and facilitate the work of these professionals. We focus on offering healthy and balanced meal plans that not only satisfy the recommended energy and nutrient intakes, but that also have a minimum cost and maximum variety of courses and food groups. Quantitative analyses that compare the meal plans yielded by SCHOOLTHY for meal plans designed by experts at hand and served in regional schools demonstrate the suitability of the proposal. Finally, we note that, thanks to its flexibility, SCHOOLTHY might be easily adapted to deal with other environments, such as hospitals, prisons and retirement homes, among others.

Este trabajo presenta una herramienta Web libre y gratuita que facilita a cualquier centro educativo la enseñanza de conceptos básicos sobre robótica y programación y que, al mismo tiempo, permite desarrollar habilidades relacionadas con el pensamiento computacional: descomposición, abstracción, reconocimiento de patrones y pensamiento algorítmico. Dicha herramienta permite diseñar y personalizar un robot a través del uso de distintos tipos de sensores. Tras su creación, dicho robot se podrá poner a prueba en un entorno de simulación mediante distintos retos. En dicho entorno podremos definir el comportamiento del robot por medio de un lenguaje de programación visual basado en bloques. Dichos bloques permiten definir las acciones a llevar a cabo por el robot en función de la información recogida por los sensores con el objetivo de superar los desafíos propuestos. This work presents a free software tool that facilitates the teaching of basic robotics and programming concepts at any educational institution. At the same time, it allows the development of computational thinking skills to be carried out: decomposition, abstraction, pattern recognition and algorithmic thinking. This tool allows the design and configuration of a robot through the specification of different types of sensors. After designing the robot, its behaviour can be simulated by means of different challenges proposed to the user. This behaviour is defined through a block-based visual programming language. Blocks allow actions that the robot has to perform based on the information gathered by the different sensors to be defined in order to pass a challenge.