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All Outputs (26)

Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution (2024)
Conference Proceeding
Marrero, A., Segredo, E., León, C., & Hart, E. (in press). Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. In Genetic and Evolutionary Computation Conference (GECCO ’24), July 14–18, 2024, Melbourne, VIC, Australia. https://doi.org/10.1145/3638529.3654028

The ability to generate example instances from a domain is important in order to benchmark algorithms and to generate data that covers an instance-space in order to train machine-learning models for algorithm selection. Quality-Diversity (QD) algorit... Read More about Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution.

Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains (2024)
Journal Article
Marrero, A., Segredo, E., Leon, C., & Hart, E. (in press). Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains. Evolutionary Computation,

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 w... Read More about Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains.

What Emotions do Pre-university Students Feel when Engaged in Computational Thinking Activities? (2023)
Journal Article
Herrero-Álvarez, R., León, C., Miranda, G., Segredo, E., Socas, Ó., Cuellar-Moreno, M., & Caballero-Juliá, D. (2023). What Emotions do Pre-university Students Feel when Engaged in Computational Thinking Activities?. International Journal of Computer Science Education in Schools, 6(2), https://doi.org/10.21585/ijcses.v6i2.180

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... Read More about What Emotions do Pre-university Students Feel when Engaged in Computational Thinking Activities?.

Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space (2023)
Conference Proceeding
Marrero, A., Segredo, E., Hart, E., Bossek, J., & Neumann, A. (2023). Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference (312-320). https://doi.org/10.1145/3583131.3590504

Generating new instances via evolutionary methods is commonly used to create new benchmarking data-sets, with a focus on attempting to cover an instance-space as completely as possible. Recent approaches have exploited Quality-Diversity methods to ev... Read More about Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space.

DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains (2023)
Journal Article
Marrero, A., Segredo, E., León, C., & Hart, E. (2023). DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains. SoftwareX, 22, Article 101355. https://doi.org/10.1016/j.softx.2023.101355

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 t... Read More about DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains.

A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem (2022)
Conference Proceeding
Marrero, A., Segredo, E., León, C., & Hart, E. (2022). A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem. In Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022 (223-236). https://doi.org/10.1007/978-3-031-14714-2_16

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... Read More about A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem.

Engaging Primary and Secondary School Students in Computer Science Through Computational Thinking Training (2022)
Journal Article
Herrero-Alvarez, R., Miranda, G., Leon, C., & Segredo, E. (2023). Engaging Primary and Secondary School Students in Computer Science Through Computational Thinking Training. IEEE Transactions on Emerging Topics in Computing, 11(1), 56-69. https://doi.org/10.1109/tetc.2022.3163650

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 informat... Read More about Engaging Primary and Secondary School Students in Computer Science Through Computational Thinking Training.

Computational Thinking and User Interfaces: A Systematic Review (2022)
Journal Article
Rijo-Garcia, S., Segredo, E., & Leon, C. (2022). Computational Thinking and User Interfaces: A Systematic Review. IEEE Transactions on Education, 65(4), 647-656. https://doi.org/10.1109/te.2022.3159765

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, computat... Read More about Computational Thinking and User Interfaces: A Systematic Review.

How do young students get enthusiastic about computational thinking activities? (2021)
Conference Proceeding
Herrero Álvarez, R., León, C., Miranda, G., Segredo, E., Socas, Ó., Cuellar-Moreno, M., …Díaz, Y. (2021). How do young students get enthusiastic about computational thinking activities?. In EDULEARN21 Proceedings. https://doi.org/10.21125/edulearn.2021.0199

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 t... Read More about How do young students get enthusiastic about computational thinking activities?.

Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem (2020)
Journal Article
Mahrach, M., Miranda, G., León, C., & Segredo, E. (2020). Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem. Mathematics, 8(11), Article 2018. https://doi.org/10.3390/math8112018

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, t... Read More about Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem.

SCHOOLTHY: Automatic Menu Planner for Healthy and Balanced School Meals (2020)
Journal Article
Segredo, E., Miranda, G., Ramos, J. M., Leon, C., & Rodriguez-Leon, C. (2020). SCHOOLTHY: Automatic Menu Planner for Healthy and Balanced School Meals. IEEE Access, 8, 113200-113218. https://doi.org/10.1109/access.2020.3003067

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... Read More about SCHOOLTHY: Automatic Menu Planner for Healthy and Balanced School Meals.

Simulador de Robótica Educativa para la promoción del Pensamiento Computacional | Educational Robotics simulator for fostering Computational Thinking (2020)
Journal Article
Ángel-Díaz, C. M., Segredo, E., Arnay, R., & León, C. (2020). Simulador de Robótica Educativa para la promoción del Pensamiento Computacional | Educational Robotics simulator for fostering Computational Thinking. RED. Revista de Educación a Distancia, 20(63), https://doi.org/10.6018/red.410191

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 pensamien... Read More about Simulador de Robótica Educativa para la promoción del Pensamiento Computacional | Educational Robotics simulator for fostering Computational Thinking.

A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution (2019)
Journal Article
Segredo, E., Lalla-Ruiz, E., Hart, E., & Voß, S. (2020). A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution. Computers and Operations Research, 117, Article 104871. https://doi.org/10.1016/j.cor.2019.104871

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 a... Read More about A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution.

Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation (2019)
Journal Article
Segredo, E., Luque, G., Segura, C., & Alba, E. (2019). Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation. IEEE Access, 7, 43915-43932. https://doi.org/10.1109/ACCESS.2019.2908562

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 for... Read More about Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation.

A Cooperative Learning Approach for the Quadratic Knapsack Problem (2018)
Conference Proceeding
Lalla-Ruiz, E., Segredo, E., & Voß, S. (2018). A Cooperative Learning Approach for the Quadratic Knapsack Problem. In Learning and Intelligent Optimization Conference (LION12) (31-35). https://doi.org/10.1007/978-3-030-05348-2_3

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 theor... Read More about A Cooperative Learning Approach for the Quadratic Knapsack Problem.

Selection methods and diversity preservation in many-objective evolutionary algorithms (2018)
Journal Article
Martí, L., Segredo, E., Sánchez-Pi, N., & Hart, E. (2018). Selection methods and diversity preservation in many-objective evolutionary algorithms. Data Technologies and Applications, https://doi.org/10.1108/dta-01-2018-0009

Purpose – 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 selecti... Read More about Selection methods and diversity preservation in many-objective evolutionary algorithms.

A novel similarity-based mutant vector generation strategy for differential evolution (2018)
Conference Proceeding
Segredo, E., Lalla-Ruiz, E., & Hart, E. (2018). A novel similarity-based mutant vector generation strategy for differential evolution. In H. Aguirre (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference 2018. https://doi.org/10.1145/3205455.3205628

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 intensificatio... Read More about A novel similarity-based mutant vector generation strategy for differential evolution.

On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems (2018)
Journal Article
Voß, S., Segredo, E., Lalla-Ruiz, E., Hart, E., & Voss, S. (2018). On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems. Expert Systems with Applications, 102, 126-142. https://doi.org/10.1016/j.eswa.2018.02.024

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... Read More about On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems.

Impact of selection methods on the diversity of many-objective Pareto set approximations (2017)
Journal Article
Martí, L., Segredo, E., Sánchez-Pi, N., & Hart, E. (2017). Impact of selection methods on the diversity of many-objective Pareto set approximations. Procedia Computer Science, 112, 844-853. https://doi.org/10.1016/j.procs.2017.08.077

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 poss... Read More about Impact of selection methods on the diversity of many-objective Pareto set approximations.