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Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains (2024)
Journal Article
Marrero, A., Segredo, E., Leon, C., & Hart, E. (online). Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains. Evolutionary Computation, https://doi.org/10.1162/evco_a_00350

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.

Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution (2024)
Presentation / Conference Contribution
Marrero, A., Segredo, E., León, C., & Hart, E. (2024, July). Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. Presented at ACM GECCO 2024, Melbourne, Australia

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.

Training future engineers: Integrating Computational Thinking and effective learning methodologies into education (2024)
Journal Article
Herrero‐Álvarez, R., León, C., Miranda, G., & Segredo, E. (2024). Training future engineers: Integrating Computational Thinking and effective learning methodologies into education. Computer Applications in Engineering Education, 32(3), Article e22723. https://doi.org/10.1002/cae.22723

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... Read More about Training future engineers: Integrating Computational Thinking and effective learning methodologies into education.

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)
Presentation / Conference Contribution
Marrero, A., Segredo, E., Hart, E., Bossek, J., & Neumann, A. (2023, July). Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space. Presented at GECCO 2023, Lisbon, Portugal

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)
Presentation / Conference Contribution
Marrero, A., Segredo, E., León, C., & Hart, E. (2022, September). A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem. Presented at Parallel Problem Solving from Nature – PPSN XVII, 17th International Conference, Dortmund, Germany

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)
Presentation / Conference Contribution
Herrero Álvarez, R., León, C., Miranda, G., Segredo, E., Socas, Ó., Cuellar-Moreno, M., Caballero-Julia, D., García, L., & Díaz, Y. (2021, July). How do young students get enthusiastic about computational thinking activities?. Presented at 13th International Conference on Education and New Learning Technologies, Online

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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
Segredo, E., Lalla-Ruiz, E., & Hart, E. (2018, July). A novel similarity-based mutant vector generation strategy for differential evolution. Presented at The Genetic and Evolutionary Computation Conference 2018 (GECCO 2018), Kyoto, Japan

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.