inproceedings by Jamal Toutouh El Alamin
Papers by Jamal Toutouh El Alamin
Lecture notes in computer science, 2024
Proceedings of the Genetic and Evolutionary Computation Conference, Jul 12, 2023
It can be expensive to label images for classification. Good classifiers or high-quality images c... more It can be expensive to label images for classification. Good classifiers or high-quality images can be trained on unlabeled data with Generative Adversarial Network (GAN) methods. We use coevolutionary algorithms with Semi-Supervised GANs (SSL-GANs) that work with a few labeled and some more unlabeled images to train both a good classifier and a high-quality image generator. A spatial coevolutionary algorithm introduces diversity into the GAN training. We use a two-dimensional grid of GANs to gain discriminator loss diversity with a distributed cell-level coevolutionary algorithm. The GAN components are exchanged between neighboring cells based on performance and population-based hyperparameter tuning. The approach is demonstrated on two separate benchmark datasets, and with only a few labels, we simultaneously achieve good classification accuracy and high generated image quality score. In addition, the generated image quality and classification accuracy are competitive to state-of-the-art methods. • Computing methodologies → Semi-supervised learning settings; Bio-inspired approaches; Neural networks.
This article presents a multi-objective variation of the problem of locating electric vehicle cha... more This article presents a multi-objective variation of the problem of locating electric vehicle charging stations in a city. The problem considers two conflicting objectives: maximizing the quality of service of the network of charging stations and minimizing the deployment cost, when installing different types of charging stations. A metaheuristic algorithm based on the Non-dominated Sorting Genetic Algorithm is proposed to address the problem. The experimental analysis is performed on a real-world case study defined in Málaga (Spain) and it compares the proposed approach with a deterministic exhaustive search method. Results show that the proposed approach computes the most competitive solutions that represent different trade-offs between the quality of service and the installation costs, improving up to 5.79% over a deterministic exhaustive search.
Proceedings of the Genetic and Evolutionary Computation Conference, Jul 12, 2023
While competitive coevolutionary algorithms are ideally suited to model adversarial dynamics, the... more While competitive coevolutionary algorithms are ideally suited to model adversarial dynamics, their complexity makes it di cult to understand what is happening when they execute. To achieve better clarity, we introduce a game named DefendIt and explore a previously developed pairwise dominance coevolutionary algorithm named PDCoEA. We devise a methodology for consistent algorithm comparison, then use it to empirically study the impact of population size, the impact of relative budget limits between the defender and attacker, and the impact of mutation rates on the dynamics and payo s. Our methodology provides reliable comparisons and records of run and multi-run dynamics. Our supplementary material also o ers enticing and detailed animations of a pair of players' game moves over the course of a game of millions of moves matched to the same run's populations' payo s.
ICERI proceedings, Nov 1, 2022
Communications in computer and information science, 2021
This is a relevant problem because the design of most cities prioritizes the use of motorized veh... more This is a relevant problem because the design of most cities prioritizes the use of motorized vehicles, which has degraded air quality in recent years, having a negative effect on urban health. Modeling, predicting, and forecasting ambient air pollution is an important way to deal with this issue because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven methods for modeling, predicting, and forecasting outdoor pollution requires an important amount of data, which may limit their accuracy. In order to deal with such a lack of data, we propose to train models able to generate synthetic nitrogen dioxide daily time series according to a given classification that will allow an unlimited generation of realistic data. The main experimental results indicate that the proposed approach is able to generate accurate and diverse pollution daily time series, while requiring reduced computational time.
Proceedings of the Genetic and Evolutionary Computation Conference, Jul 12, 2023
While competitive coevolutionary algorithms are ideally suited to model adversarial dynamics, the... more While competitive coevolutionary algorithms are ideally suited to model adversarial dynamics, their complexity makes it di cult to understand what is happening when they execute. To achieve better clarity, we introduce a game named DefendIt and explore a previously developed pairwise dominance coevolutionary algorithm named PDCoEA. We devise a methodology for consistent algorithm comparison, then use it to empirically study the impact of population size, the impact of relative budget limits between the defender and attacker, and the impact of mutation rates on the dynamics and payo s. Our methodology provides reliable comparisons and records of run and multi-run dynamics. Our supplementary material also o ers enticing and detailed animations of a pair of players' game moves over the course of a game of millions of moves matched to the same run's populations' payo s.
ICERI proceedings, Nov 1, 2022
Communications in computer and information science, 2021
This is a relevant problem because the design of most cities prioritizes the use of motorized veh... more This is a relevant problem because the design of most cities prioritizes the use of motorized vehicles, which has degraded air quality in recent years, having a negative effect on urban health. Modeling, predicting, and forecasting ambient air pollution is an important way to deal with this issue because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven methods for modeling, predicting, and forecasting outdoor pollution requires an important amount of data, which may limit their accuracy. In order to deal with such a lack of data, we propose to train models able to generate synthetic nitrogen dioxide daily time series according to a given classification that will allow an unlimited generation of realistic data. The main experimental results indicate that the proposed approach is able to generate accurate and diverse pollution daily time series, while requiring reduced computational time.
ICERI proceedings, Nov 1, 2019
ICERI proceedings, Nov 1, 2019
EDULEARN proceedings, Jul 1, 2018
EDULEARN proceedings, Jul 1, 2018
is a long and arduous process, which has only been possible thanks to the help, support, and cont... more is a long and arduous process, which has only been possible thanks to the help, support, and contribution that I received from many people. I would like to thank my supervisor, Prof. Enrique Alba, for encouraging me to perform this work, for guiding me, and most importantly, for his patience. To the NEO Research Group members, not only to the current people in the team, but also to some of them who have already left and the visitors who have stayed with us just for short periods of time. I have learned uncountable new things during our discussions, meetings, and coffee breaks. I have found here more than colleagues, we are a great family (Briseida, Christian
EDULEARN proceedings, Mar 1, 2017
Results of the experience of the application of gamification to Engineering courses at the Univer... more Results of the experience of the application of gamification to Engineering courses at the University of Málaga, Spain, are presented. The goal is to fight the lack of motivation and reduce the background unevenness detected in these degrees. A detailed description of the implementation is provided, dividing it into three phases: preparation, execution and monitoring. Results of the games (statistics) and students' feedback collected so far are shown for three different courses. They indicate that a good design of the games and award system is necessary to avoid loss of interest from the students. Moreover, adaptation to the context of the course (e.g., number of students) is crucial to guarantee success.
Communications in computer and information science, 2022
Communications in computer and information science, 2021
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inproceedings by Jamal Toutouh El Alamin
Papers by Jamal Toutouh El Alamin