Eduardo Segredo

Universidad de La Laguna | ULL · Departamento de Ingeniería Informática y de Sistemas

Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well with respect to a target algorithm belonging to a predefined portfolio but are also diverse with resp...

This article examines the effectiveness and interest generated among primary and secondary education students through activities aimed at developing Computational Thinking skills, in the context of the coronavirus disease 2019 pandemic. The shift to online or hybrid learning models posed a significant challenge for educators, particularly those lac...

La última reforma educativa de nuestro país incorpora entre sus competencias la resolución de problemas a través del pensamiento computacional. Esto supone un cambio de paradigma a nivel formativo: las personas no serán meras usuarias de la tecnología sino que, desde jóvenes, adquirirán habilidades para ser creadoras y desarrolladoras en este mundo...

Emotions affect how we acquire knowledge, being one of the causes of the demotivation generated at the time of studying a new field. Computer Science does not always pique the interest of young people, so we carry out an analysis of emotions that are present in primary and secondary school students, around 8-9 years old and 12-13 years old, who eng...

En 2016, se puso en marcha el Aula Cultural de Pensamiento Computacional de la Universidad de La Laguna para dar respuesta social a la confusión que surge al integrar las Tecnologías de la Información y las Comunicaciones en la educación. Es importante distinguir entre los términos “Alfabetización Digital”, “Pensamiento Computacional” y “Ciencias d...

To advance research in the development of optimisation algorithms, it is crucial to have access to large test-beds of diverse and discriminatory instances from a domain that can highlight strengths and weaknesses of different algorithms. The DIGNEA tool enables diverse instance suites to be generated for any domain, that are also discriminatory wit...

We propose a new approach to generating synthetic instances in the knapsack domain in order to fill an instance-space. The method uses a novelty-search algorithm to search for instances that are diverse with respect to a feature-space but also elicit discriminatory performance from a set of target solvers. We demonstrate that a single run of the al...

Computational thinking could be described as the thought processes involved in formulating problems and representing their solutions in such a way that these solutions can be executed by an information processing agent (either a human, a computer, or combinations of both). Therefore, this process involves learning to think about how to represent an...

Although Computer Science has grown to become one of the most highly demanded professional careers, every year, only a small percentage of students choose a degree directly related to Computer Science. Perhaps the problem lies in the lack of information that society has about Computer Science itself, and particularly about the work computer scienti...

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 huma...

This paper presents a study of the emotions that are produced in pre-university students when performing Computational Thinking activities. Two strategies are compared in which the guided and discovery methodologies are interspersed. It is concluded that positive and ambiguous emotions are mainly produced, while negative ones have relatively low in...

This paper presents a study of the emotions that are produced in pre-university students when performing Computational Thinking activities. In the absence of an official document that deals what content of Computational Thinking should be taught at the national level, we carefully selected a set of activities called Piens@ Computacion@ULLmente, tha...

This work presents a curricular proposal of activities to include computational thinking skills in pre-university studies for students from 8-9 years old and 12-13 years old. This proposal is made for two modalities, one guided and the other by discovery, in which the development of solutions to different problems is proposed by designing an algori...

A multi-objective formulation of the Menu Planning Problem, which is termed the Multi-objective Menu Planning Problem, is presented herein. Menu planning is of great interest in the health field due to the importance of proper nutrition in today’s society, and particularly, in school canteens. In addition to considering the cost of the meal plan as...

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 f...

Encouraging healthy and balanced diet plans is one of the most important action points for governments around the world. Generating healthy, balanced and inexpensive menu plans that fulfil all the recommendations given by nutritionists is a complex and time-consuming task; because of this, computer science has an important role in this area. This p...

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, nutritio...

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....

The success of search-based optimisation algorithms depends on appropriately balancing exploration and exploitation mechanisms during the course of the search. We introduce a mechanism that can be used with Differential Evolution (de) algorithms to adaptively manage the balance between the diversification and intensification phases, depending on cu...

With the raise of diseases related with unhealthy lifestyles such as heart-attacks, overweight, diabetes, etc., encouraging healthy and balanced patterns in the population is one of the most important action points for governments around the world. Furthermore, it is actually even a more critical situation when a high percentage of patients are chi...

Traffic congestion, and the consequent loss of time, money, quality of life and higher pollution, is currently one of the most important problems in cities, and several approaches have been proposed to reduce it. In this paper we propose a novel formulation of the Traffic Light Scheduling Problem in order to alleviate it. This novel formulation of...

The Quadratic Knapsack Problem (QKP) is a well-known optimization problem aimed to maximize a quadratic objective function subject to linear capacity constraints. It has several applications in different fields such as telecommunications, graph theory, logistics, hydrology and data allocation, among others. In this paper, we propose the application...

En este documento se detalla el proyecto de investigación de una tesis doctoral relacionada con la computación evolutiva, el aprendizaje automatizado y su aplicación a la resolución de un subconjunto de problemas de optimización.

One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversit...

The mutant vector generation strategy is an essential component of Differential Evolution (de), introduced to promote diversity, resulting in exploration of novel areas of the search space. However, it is also responsible for promoting intensification, to improve those solutions located in promising regions. In this paper we introduce a novel simil...

Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More recently, an adaptation of the algorithm has been proposed that enables it to deal with continuous search spaces. We extend this work in two ways. Firstly, a novel leader repl...

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the e...

The transformation of traditional education into a Sensitive, Manageable, Adaptable, Responsive and Timely (SMART) education involves the comprehensive modernisation of all educational processes. For such a transformation, smart pedagogies are needed as a methodological issue while smart learning environments represent the technological issue, both...

Differential Evolution (de) has shown to be a promising global optimisation solver for continuous problems, even for those with a large dimensionality. Different previous works have studied the effects that a population initialisation strategy has on the performance of de when solving large scale continuous problems, and several contradictions have...

We present novel algorithmic schemes for dealing with large scale continuous problems. They are based on the recently proposed population-based meta-heuristics Migrating Birds Optimisation (MBO) and Multi-leader Migrating Birds Optimisation (MMBO), that have shown to be effective for solving combinatorial problems. The main objective of the current...

Core subjects by field of knowledge for official University studies have been established in Annex II of the RD 1393/2007. Computer Science appears only in Engineering and Architecture Degrees. It is therefore necessary that the training received by high school students in the Computer field is not limited only to the intrinsic knowledge of current...

Choosing the correct algorithm to solve a problem still remains an issue 40 years after the Algorithm Selection Problem was first posed. Here we propose a hyper-heuristic which can apply one of two meta-heuristics at the current stage of the search. A scoring function is used to select the most appropriate algorithm based on an estimate of the impr...

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 id...

In recent years, Multi-Objective Evolutionary Algorithms (moeas) that consider diversity as an objective have been used to tackle single-objective optimisation problems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To...

Differential evolution (DE) is a simple yet effective metaheuristic specially suited for real-parameter optimization. The most advanced DE variants take into account the feedback obtained in the self-optimization process to modify their internal parameters and components dynamically. In recent years, some controversies have arisen regarding the ada...

One of the main disadvantages of Evolutionary Algorithms (EAs) is that they converge towards local optima for some problems. In recent years, diversity-based multi-objective EAs have emerged as a promising technique to prevent from local optima stagnation when optimising single-objective problems. An additional drawback of EAs is the large dependen...

Differential Evolution (DE) is a very efficient meta-heuristic for optimization over continuous spaces which has gained much popularity in recent years. Several parameter control strategies have been proposed to automatically adapt its internal parameters. The most advanced DE variants take into account the feedback obtained in the optimization pro...

Packing problems are np-hard problems with several practical applications. A variant of a 2d Packing Problem (2dpp) was proposed in the gecco 2008 competition session. In this paper, Memetic Algorithms (mas) and Hyperheuristics are applied to a multiobjectivised version of the 2dpp. Multiobjectivisation is the reformulation of a mono-objective prob...

One of the most commonly known weaknesses of Evolutionary Algorithms (eas) is the large dependency between the values selected for their parameters and the results. Parameter control approaches that adapt the parameter values during the course of an evolutionary run are becoming more common in recent years. The aim of these schemes is not only to i...

The Unknown Complexity Formula Problem (ucfp) is a particular case of the symbolic regression problem in which an analytical complexity formula that fits with data obtained by multiple executions of certain algorithm must be given. In this work, a set of modifications has been added to the standard Genetic Programming (gp) algorithm to deal with th...

The maintenance of a proper diversity is an important issue for the correct behavior of Evolutionary Algorithms (EAs). The loss of diversity might lead to stagnation in suboptimal regions, producing the effect known as “premature convergence”. Several methods to avoid premature convergence have been previously proposed. Among them, the use of Multi...

Multiobjectivisation transforms a mono-objective problem into a multiobjective one. The main aim of multiobjectivisation is to avoid stagnation in local optima by changing the landscape of the original fitness function. In this contribution, an analysis of different multiobjectivisation approaches has been performed. It has been carried out with a...

The Frequency Assignment Problem (fap) is one of the key issues in the design of Global System for Mobile Communications (gsm) networks. The formulation of the fap used here focuses on aspects that are relevant to real gsm networks. In this paper, we adapt a parallel model to tackle a multiobjectivised version of the fap. It is a hybrid model which...

Evolutionary Algorithms (EAs) are one of the most popular strategies for solving optimisation problems. To define a configuration of an EA several components and parameters must be specified. Therefore, one of the main drawbacks of EAs is the complexity of their parameter setting. Another problem is that EAs might have a tendency to converge toward...

Evolutionary Algorithms (EAs) are one of the most popular strategies for solving optimisation problems. Several variants of EAs are seen to exist. They usually have several components and parameters which must be fixed. Therefore, one of the main drawbacks of EAs is the complexity of their parameter setting. Multiobjectivisation consists in the ref...

Antenna Positioning Problem (APP) is an NP-Complete Optimisation Problem which arises in the telecommunication field. Its aim is to identify the infrastructures required to establish a wireless network. A well-known mono-objective version of the problem has been used. The best-known approach to tackle such a version is a problem-dependent strategy....

Bin Packing problems are NP-hard problems with many practical applications. A variant of a Bin Packing Problem was proposed in the GECCO 2008 competition session. The best results were achieved by a mono-objective Memetic Algorithm (MA). In order to reduce the execution time, it was parallelised using an island-based model. High quality results wer...

This work presents a set of approaches used to deal with the Frequency Assignment Problem (FAP), which is one of the key issues in the design of Global System for Mobile Communications (GSM) networks. The used formulation of the FAP is focused on aspects which are relevant for real-world GSM networks. The best up to date frequency plans for the con...

Multiobjectivisation transforms a mono-objective problem into a multi-objective one. The main aim of multiobjectivisation is to avoid stagnation in local optima, by changing the landscape of the original fitness function. In this work, an analysis of different multiobjectivisations has been performed. It has been carried out with a set of scalable...

Evolutionary Algorithms (eas) are one of the most popular strategies for solving optimisation problems. One of the main drawbacks of eas is the complexity of their parameter setting. This setting is mandatory to obtain high quality solutions. In order to deal with the parameterisation of an ea, hyperheuristics can be applied. They manage the choice...

Multiobjectivisation is a technique which transforms a mono-objective optimisation problem into a multi-objective one with the aim of avoiding stagnation. The transformation can be performed by the addition of artificial objectives or by the decomposition of the original objective function. Several well-known multiobjectivisation schemes, based on...

The parameter values of a Multi-objective Evolutionary Algorithm greatly determine the behavior of the algorithm to find good solutions within a reasonable time for a particular problem. In general, static strategies consume lots of computational resources and time. In this work, a tool is used to develop a static strategy to solve the parameter se...

This work presents the application of a parallel cooperative optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation implies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configuration...

ULL::A-Team tool is a library that provides a skeleton to solve multi-objective optimization problems by applying evolutionary algorithms. In addition to providing sequential implementations of some of the best-known evolutionary algorithms, the skeleton provides great flexibility in obtaining parallel schemes. This flexibility is achieved by speci...

This work presents a new parallel model for the solution of multi-objective optimization problems. The model is based on the cooperation of a set of evolutionary algorithms. The main aim is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths o...