Jamal Toutouh

• PhD
• PostDoc Position at Massachusetts Institute of Technology

Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes generate frequent topology changes and network fragmentations. For these reasons, and taking into account that there is no central manager entity, routing...

This work tackles the problem of reducing the power consumption of the OLSR routing protocol in vehicular networks. Nowadays, energy-aware and green communication protocols are important research topics, specially when deploying wireless mobile networks. This article introduces a fast automatic methodology to search for energy-efficient OLSR config...

The emerging field of vehicular ad hoc networks (VANETs) deals with a set of communicating vehicles which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, it is crucial to make an optimal configuration of the communication protocols previously to the final network deployment. This way, a huma...

Vehicular ad hoc networks (VANETs) provide the communications required to deploy Intelligent Transportation Systems (ITS). In the current state of the art in this field there is a lack of studies on real outdoor experiments to validate the new VANETs protocols and applications proposed by designers. In this work we have addressed the definition of...

Vehicular ad hoc networks (VANETs) provide the communications required to deploy Intelligent Transportation Systems (ITS). In the current state of the art in this field there is a lack of studies on real outdoor experiments to validate thenew VANETs protocols and applications proposed by designers. In this work we have addressed the definition of a...

This work addresses the reduction of power consumption of the AODV routing protocol in vehicular networks as an optimization problem. Nowadays, network designers focus on energy-aware communication protocols, specially to deploy wireless networks. Here, we introduce an automatic method to search for energy-efficient AODV configurations by using an...

The increasing number of wireless communication technologies and standards bring immense opportunities and challenges to provide seamless connectivity in Hybrid Vehicular Networks (HVNs). HVNs could not only enhance existing applications but could also spur an array of new services. However, due to sheer number of use cases and applications with di...

Vehicular communication networks represent both an opportunity and a challenge for providing smart mobility services by using a hybrid solution that relies on cellular connectivity and short range communications. The evaluation of this kind of network is overwhelmingly carried out in the present literature with simulations. However, the degree of r...

This work tackles the problem of reducing the power consumption of the OLSR routing protocol in vehicular networks. Nowadays, energy-aware and green communication protocols are important research topics, specially when deploying wireless mobile networks. This article introduces a fast automatic methodology to search for energy-efficient OLSR config...

Vehicular ad hoc networks (VANETs) allow vehicles to exchange warning messages with each other. These specific kinds of networks help reduce hazardous traffic situations and improve safety, which are two of the main objectives in developing Intelligent Transportation Systems (ITS). For this, the performance of VANETs should guarantee the delivery o...

This article describes the application of a multiobjective evolutionary algorithm for locating roadside infrastructure for vehicular communication networks over realistic urban areas. A multiobjective formulation of the problem is introduced, considering quality-of-service and cost objectives. The experimental analysis is performed over a real map...

This article analyzes the use of two parallel multi-objective soft computing algorithms to automatically search for high-quality settings of the Ad hoc On Demand Vector routing protocol for vehicular networks. These methods are based on an evolutionary algorithm and on a swarm intelligence approach. The experimental analysis demonstrates that the c...

Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes generate frequent topology changes and network fragmentations. For these reasons, and taking into account that there is no central manager entity, routing...

The emerging field of vehicular ad hoc networks (VANETs) deals with a set of communicating vehicles which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, it is crucial to make an optimal configuration of the communication protocols previously to the final network deployment. This way, a huma...

This paper addresses the optimization of urban infrastructure for e-scooter mobility through a multi-criteria approach. The proposed problem considers redesigning road infrastructure to integrate e-scooters into a city’s multimodal transportation system. The objectives involve improving cycle lane coverage for e-scooters while minimizing installati...

This article presents a parallel-distributed implementation of the Lipizzaner framework for multiobjective coevolutionary Generative Adversarial Networks training. A specific design is proposed following the messagge passing paradigm to execute in high performance computing infrastructures. The implementation is validated for the generation of hand...

Adversarial Evolutionary Learning (AEL) is concerned with competing adversaries that are adapting over time. This competition can be defined as a minimization–maximization problem. Different methods exist to model the search for solutions to this problem, such as the Competitive Coevolutionary Algorithm, Multi-agent Reinforcement Learning, Adversar...

Competitive coevolutionary algorithms are used to model adversarial dynamics. The diversity of the adversarial populations can be changed with a spatial topology. To achieve more clarity in how a spatial topology impacts performance and complexity we introduce a spatial topology to a pairwise dominance coevolutionary algorithm named PDCoEA. The new...

There is an increasing interest in alternative vehicle mobility, such as electric scooters (e-scooters). E-scooters are getting attention not only for their environmental impact but also because they are easy to ride on. However, could our current infrastructure support e-scooter trips? Could e-scooter offer a better way to move on our present stre...

Cartographic information is key in urban city planning and management. Deep neural networks allow detecting/extracting buildings from aerial images to gather this cartographic information. This article explores the application of deep neural networks architectures to address automatic building extraction. The results reported that UNet-based archit...

Security and emergency services are among the biggest concerns for both authorities and citizens. Better adapting those services to inhabitants is a key goal for smart cities. All emergency services have their own peculiarities, and in particular, the control of police patrols in urban areas is a complex problem connected to the dynamic vehicle rou...

Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors gather smart wireless devices (Bluetooth and Wi-Fi) anonymous informatio...

El crecimiento de las ciudades orientadas al automóvil está planteando nuevos problemas de salud urbana derivados del aumento de la contaminación asociada a la movilidad. Esto exige respuestas rápidas para crear entornos sostenibles desde el punto de vista medioambiental. Las zonas de bajas emisiones son una opción sencilla y económica, pero alguna...

This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluati...

This article presents a multiobjective variation of the problem of locating electric vehicle charging stations (EVCS) in a city known as the Multiobjective Electric Vehicle Charging Stations Locations (MO-EVCS-L) problem. MO-EVCS-L considers two conflicting objectives: maximizing the quality of service of the charging station network and minimizing...

This article presents an exact approach for solving the problem of locating electric vehicle charging stations in a city, whose goal is upon minimizing the distance citizens must span to charge their vehicles. Mixed integer programming formulations are presented for two variants of the problem: relaxed (i.e., without considering electrical constrai...

The increase in life expectancy is undoubtedly a social achievement. If we want an inclusive and integrating society, the inclusion of the age perspective is key when planning the city and its services. Accordingly, this is reflected in the Sustainable Development Goals (SDGs), especially in SDG 11.2, which aims to provide and expand access to publ...

This research presents a parallel/distributed approach for automatically searching the hyperparameters configuration for generative artificial neural networks (GANs). This search is challenging because GANs simultaneously train two deep neural networks. The proposed system applies the iterated racing approach, taking advantage of parallel/distribut...

This article presents a parallel/distributed methodology for the intelligent search of the hyperparameters configuration for generative artificial neural networks (GANs). Finding the configuration that best fits a GAN for a specific problem is challenging because GANs simultaneously train two deep neural networks. Thus, in general, GANs have more c...

In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in deman...

This article introduces Random Error Sampling-based Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures. RESN combines an evolutionary algorithm with a training-free evaluation approach. The results show that RESN achieves state-of-the-art error performance while reducing by half the computational time...

This article presents the problem of locating electric vehicle (EV) charging stations in a city by defining the Electric Vehicle Charging Stations Locations (EV-CSL) problem. The idea is to minimize the distance the citizens have to travel to charge their vehicles. EV-CSL takes into account the maximum number of charging stations to install and the...

This article presents the problem of locating electric vehicle (EV) charging stations in a city by defining the Electric Vehicle Charging Stations Locations (EV-CSL) problem. The idea is to minimize the distance the citizens have to travel to charge their vehicles. EV-CSL takes into account the maximum number of charging stations to install and the...

Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as mode and discriminator collapse. Similar pathologies have been studied and addressed in competitive evolutionary computation by increased diversity. We study a system, Lipizzaner, that combines spatial coevolution with gradient-based learning to improve the...

This article introduces Random Error Sampling-based Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures. RESN combines an evolutionary algorithm with a training-free evaluation approach. The results show that RESN achieves state-of-the-art error performance while reducing by half the computational time...

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse, which mainly arise from a lack of diversity in their adversarial interactions. Co-evolutionary GAN (CoE-GAN) training algorithms have shown to be resilient to these pathologies. This article introduces Mustangs, a spatially distributed CoE-G...

Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcut...

Power supply is one of the basic needs in modern smart homes. Computer-aid tools help optimizing energy utilization, contributing to sustainable goals of modern societies. For this purpose, this article presents a mathematical formulation to the household energy planning problem and a specific resolution method to build schedules for using deferrab...

Power supply is one of the basic needs in modern smart homes. Computer-aid tools help optimizing energy utilization, contributing to sustainable goals of modern societies. For this purpose, this article presents a mathematical formulation to the household energy planning problem and a specific resolution method to build schedules for using deferrab...

Urbanization trends worldwide show a clear preference for motorized road mobility, which has led to a degradation of air quality in recent years. Modelling and forecasting ambient air pollution is a relevant problem because it helps decision-makers and urban city planners understand this phenomenon, which is a significant threat to citizens' health...

The design of the bus network is a complex problem in modern cities, since different conflicting objectives have to be considered, from both the perspective of bus companies and the citizens. This article presents a multiobjective model for designing a sustainable public transportation network that simultaneously optimizes the covered travel demand...

This article presents a multiobjective evolutionary approach for computing flight plans for a fleet of unmanned aerial vehicles to perform exploration and surveillance missions. The static off-line planning subproblem is addressed, which is useful to determine initial flight routes to maximize the explored area and the surveillance of points of int...

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overal...

The design of most cities prioritizes the use of motorized vehicles, having a negative effect on urban health. A major concern in the European Union (EU) is air pollution, especially nitrogen dioxide (NO2), which causes many inhabitants health problems and decreases the quality of life. A non-expensive way to reduce pollutants is implementing road...

Generative adversarial networks (GANs) exhibit training pathologies that can lead to convergence-related degenerative behaviors, whereas spatially-distributed, coevolutionary algorithms (CEAs) for GAN training, e.g. Lipizzaner, are empirically robust to them. The robustness arises from diversity that occurs by training populations of generators and...

The urban population is aging and the elderly people desire to age in place and to continue in the environments chosen by them. Accordingly, the environment should be healthy-age orientated, improving health and fulfilling the United Nations Global Goals, including the aging-related ones. Using the case study of Madrid, the biggest city in Spain, t...

Modeling, predicting, and forecasting ambient air pollution is an important way to deal with the degradation of the air quality in our cities because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven modeling, predicting, and forecasting outdoor pollution metho...

This article presents an approach using parallel/distributed generative adversarial networks for image data augmentation, applied to generate COVID-19 training samples for computational intelligence methods. This is a relevant problem nowadays, considering the recent COVID-19 pandemic. Computational intelligence and learning methods are useful tool...

Recurrent neural networks are good at solving prediction problems. However, finding a network that suits a problem is quite hard because their performance is strongly affected by their architecture configuration. Automatic architecture optimization methods help to find the most suitable design, but they are not extensively adopted because of their...

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...

Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maintenance in the populations of generators and discriminators during the training process. The method studied here coevolves sub-populations on each cell of a spatial grid o...

Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maintenance in the populations of generators and discriminators during the training process. The method studied here coevolves sub-populations on each cell of a spatial grid o...

The PDF version of our presentation during GECCO 2020.

Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks. In this study, we a...

This article presents the advances in the design and implementation of a recommendation systemfor planning the use of household appliances, focused on improving energy efficiency from the point of view ofboth energy companies and end-users. The system proposes using historical information and data from sensorsto define instances of the planning prob...

Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Programming for Cyber Security enco...

Population concentration in cities brings new risks as an increase in pollution, which causes urban health problems. In order to address this problem, traffic reduction measures are being implemented as pedestrianization areas; they are the definition of Low Emissions Zones (LEZs). When the effectiveness of these types of measures is in doubt, smar...

We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resource requirements, e.g., time and memory. We ask how much data reduction impacts generator performance and gauge the additive value of generator ensembles. In addition to c...

Population concentration in cities brings new risks as an increase in pollution, which causes urban health problems. In order to address this problem, traffic reduction measures are being implemented, as pedestrianization areas and the definition of Low Emissions Zones (LEZ). When the effectiveness of these types of measures is in doubt, smart city...

Generative adversarial networks (GANs) are widely used to learn generative models. GANs consist of two networks, a generator and a discriminator, that apply adversarial learning to optimize their parameters. This article presents a parallel/distributed implementation of a cellular competitive coevolutionary method to train two populations of GANs....

Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Programming for Cyber Security enco...

We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resource requirements, e.g. time and memory. We ask how much data reduction impacts generator performance and gauge the additive value of generator ensembles. In addition to co...

Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks. In this study, we a...

This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city...

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overal...

This article presents the application of data analysis and computational intelligence techniques for evaluating the air quality in the center of Madrid, Spain. Polynomial regression and deep learning methods to analyze the time series of nitrogen dioxide concentration, in order to evaluate the effectiveness of Madrid Central, a set of road traffic...

With the increase of population living in urban areas, many transportation-related problems have grown very rapidly. Pollution causes many inhabitants health problems. A major concern for the International Community is pollution, which causes many inhabitants health problems. Accordingly, and under the risk of fines, countries are required to reduc...

This article presents the advances in the design and implementation of a recommendation system for planning the use of household appliances, focused on improving energy efficiency from the point of view of both energy companies and end-users. The system proposes using historical information and data from sensors to define instances of the planning...

This article presents the advances in the design and implementation of a recommendation system for planning the use of household appliances, focused on improving energy efficiency from the point of view of both energy companies and end-users. The system proposes using historical information and data from sensors to define instances of the planning...

Emerging Vehicle-to-Everything (V2X) applications such as Advanced Driver Assistance Systems (ADAS) and Connected and Autonomous Driving (CAD) requires an excessive amount of data by vehicular sensors, collected, processed, and exchanged in real-time. A heterogeneous wireless network is envisioned where multiple Radio Access Technologies (RATs) can...

During the day of October 7-8, the congress has invited experts from the sector of Smart Cities to organize a sectoral debate. This debate will be composed of prestigious companies in the sector, Public Administration, as well as specialized consultants. The aim is to give a business point of view around Smart Cities. CYTED is the Ibero-American Pr...