Enrique Alba

• PhD in Computer Science (IT)
• University of Malaga

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...

The smart city concept refers principally to employing technology to deal with different problems surrounding the city and the citizens. Urban mobility is one of the most challenging aspects considering the logistical complexity as well as the ecological relapses. More specifically, parking is a daily tedious task that citizens confront especially...

Solving problems of high dimensionality (and complexity) usually needs the intense use of technologies, like parallelism, advanced computers and new types of algorithms. MapReduce (MR) is a computing paradigm long time existing in computer science that has been proposed in the last years for dealing with big data applications, though it could also...

In the traffic light scheduling problem, the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with e...

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...

The continuous evolution of economy hinders the decision-making process in this field. The former requires sophisticated techniques, and thus, manual and empirical methods are becoming increasingly obsolete. In this paper, we propose a computationally-supported approach that performs an economic profiling of cities based on their economic features...

Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substantial communication load, which jeopardises the functioning efficiency. The difficulty of reducing this overhead stan...

Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substantial communication’s load, which jeopardises the functioning efficiency. The difficulty of reducing this overhead st...

The number of devices, from smartphones to IoT hardware, interconnected via the Internet is growing all the time. These devices produce a large amount of data that cannot be analyzed in any data center or stored in the cloud, and it might be private or sensitive, thus precluding existing classic approaches. However, studying these data and gaining...

This work proposes a novel approach to evaluate and analyze the behavior of multi-population parallel genetic algorithms (PGAs) when running on a cluster of multi-core processors. In particular, we deeply study their numerical and computational behavior by proposing a mathematical model representing the observed performance curves. In them, we disc...

We model the cognitive complexity reduction of a method as an optimization problem where the search space contains all sequences of Extract Method refactoring opportunities. We then propose a novel approach that searches for feasible code extractions allowing developers to apply them, all in an automated way. This will allow software developers to...

Quantum-inspired metaheuristics are solvers that incorporate inspired, but still not real, quantum mechanics’ principles into classical-approximate algorithms using non-quantum machines. Due to the uniqueness of quantum principles, the inspiration of quantum phenomena and the way it is done in fundamentally different non-quantum systems rather than...

Cellular networks are one of today’s most popular means of communication. This fact has made the mobile phone industry subject to a huge scientific and economic competition, where the quality of service is key. Such a quality is measured on the basis of reliability, speed and accuracy when delivering a service to a user no matter his location or be...

Quantum computers are unique systems based on peculiar properties from quantum physics, such as entangle-ment and superposition that allow them to provide unique computational performances. Quantum computing is meant to be revolutionary in many senses and fields. Some quantum machines have already been devised, but their accessibility or affordabil...

Quantum computing (QC) promises more powerful computers than classical ones and faster solutions to complex problems. Currently, there are two main paradigms in QC: quantum gate computers and quantum annealers. Both technologies are well established and face similar problems: scalability of the number of qubits, robustness of QC, and access to quan...

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...

This work aims at giving an updated vision on the successful combination between Metaheuristics and Software Engineering (SE). Mostly during the 90s, varied groups of researchers dealing with search, optimization, and learning (SOL) met SE researchers, all of them looking for a quantified manner of modeling and solving problems in the software fiel...

Solving problems of high dimensionality (and complexity) usually needs the intense use of technologies, like parallelism, advanced computers and new types of algorithms. MapReduce (MR) is a computing paradigm long time existing in computer science that has been proposed in the last years for dealing with big data applications, though it could also...

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 work proposes a novel approach to evaluate and analyze the behavior of multi-population parallel genetic algorithms (PGAs) when running on a cluster of multi-core processors. In particular, we deeply study their numerical and computational behavior by proposing a mathematical model representing the observed performance curves. In them, we disc...

Traffic congestion is one of the main concerns of citizens living in big cities. In this article we propose a novel system, called Yellow Swarm, with the aim of improving road traffic at a low cost and easy implementation by using a set of LED panels placed throughout the city in order to spread road traffic over its streets. The system is optimall...

Metaheuristics for solving multiobjective problems can provide an approximation of the Pareto front in a short time, but can also have difficulties finding feasible solutions in constrained problems. Integer linear programming solvers, on the other hand, are good at finding feasible solutions, but they can require some time to find and guarantee th...

In many real-world optimization problems, like the traffic light scheduling problem tackled here, the evaluation of candidate solutions requires the simulation of a process under various scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Prev...

In multiobjective optimization, the result of an optimization algorithm is a set of efficient solutions from which the decision maker selects one. It is common that not all the efficient solutions can be computed in a short time and the search algorithm has to be stopped prematurely to analyze the solutions found so far. A set of efficient solution...

Recurrent neural networks (RNNs) are a powerful approach for time series prediction. However, their performance is strongly affected by their architecture and hyperparameter settings. The architecture optimization of RNNs is a time-consuming task, where the search space is typically a mixture of real, integer and categorical values. To allow for sh...

This book constitutes the refereed proceedings of the 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020, which was cancelled due to the COVID-19 pandemic, amalgamated with CAEPIA 2021, and held in Malaga, Spain, during September 2021. The 25 full papers presented were carefully selected from 40 submissions. The Co...

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...

In this article, we encompass an analysis of the recent advances in parallel genetic algorithms (PGAs). We have selected these algorithms because of the deep interest in many research fields for techniques that can face complex applications where running times and other computational resources are greedily consumed by present solvers, and PGAs act...

In this work, we solve the problem of finding the best locations to place stations for depositing/collecting shared bicycles. To do this, we model the problem as the p-median problem, that is a major existing localization problem in optimization. The p-median problem seeks to place a set of facilities (bicycle stations) in a way that minimizes the...

The concept of a smart city has recently gained attention in academic, industry, and governments. Smart cities could be considered as urban areas that use data collection sensors and digital technologies which cooperate to create benefits for citizens in terms of well being, inclusion and participation, environmental quality, and intelligent develo...

Optimization arises everywhere in industrial and engineering fields, with complex and time-consuming problems to be solved. Exact search techniques cannot afford practical solutions for most of the real-life problems in reasonable time-bound. Metaheuristics proved to be numerically efficient solvers for such problems in terms of solution quality, h...

Cellular genetic algorithms (cGAs) are a class of evolutionary algorithms in which the population is structured as a grid and interactions between individuals are restricted to the neighbourhood. Like any other optimisation algorithm, the cGA’s efficiency lies in its ability to find an adequate balance between its exploratory and exploitive capabil...

Vehicle traffic congestion is an increasing concern in metropolitan areas, with negative implications for health, environment, and economy. Researchers, city managers, and entrepreneurs have shown great interest in Smart Mobility, and several approaches have been proposed to reduce these non-desired effects. In this work, we focus on using the exis...

Archery is one of these sports in which the athletes repeat the same body postures over and over again. This means that tiny wrong habits could cause serious long-term health injuries. Consequently, learning a correct shooting technique is very important for both beginner archers and elite athletes. In this work, we present a system that uses machi...

Recurrent neural networks (RNNs) are a powerful approach for time series prediction. However, their performance is strongly affected by their architecture and hyperparameter settings. The architecture optimization of RNNs is a time-consuming task, where the search space is typically a mixture of real, integer and categorical values. To allow for sh...

Parallelism arises as an attractive option when Multi-Objective Evolutionary Algorithms (MOEAs) demand an intensive use of CPU or memory. The computational complexity of a MOEA depends on the scalability of its input parameters (i.e., the population size, number of decision variables, objectives, etc.) and on the computational cost of evaluating th...

In 2019, around 57% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user. So there is an enormous interconnected computational power held by users all around the world. Also, it is estimated that Internet users spend more than six and a...

In 2019, around 57\% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user. So there is an enormous interconnected computational power held by users all around the world. Also, it is estimated that Internet users spend more than six and a...

Recently, energy efficiency has gained attention from researchers interested in optimizing computing resources. Solving real-world problems using optimization techniques (such as metaheuristics) requires a large number of computing resources and time, consuming an enormous amount of energy. However, only a few and limited research efforts in studyi...

Smart City is a recent concept that is gaining momentum in public opinion, and thus, it is making its way into the agendas of researchers and city authorities all over the world. However, there is no consensus of what exactly is a smart city, and academic research is, at best, building applications in numerous silos. This paper explores the compute...

This paper focuses on Particle Swarm Optimization (PSO) applied to the DNA fragment assembly problem. Existing PSO algorithms for this permutation-based combinatorial problem use the Smaller Position Value (SPV) rule to transform continuous vectors into permutations of integers. However, this approach has limitations and is not suitable for this NP...

Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment due to prompt and elastic access to huge amounts of computing resources. Autoscalers are middleware-level software componen...

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...

In this paper, we analyze the neighborhood effect in the selection of parents on an evolutionary algorithm. In this line, we compare a cellular genetic algorithm (cGA), which intrinsically uses the neighbor notion in the mating process, with a modified genetic algorithm including the concept of neighborhood in the selection of parents. Additionally...

The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In t...

The goal in Robust Optimization is to optimize not only the quality of the solutions but also the variation of this quality with the uncertain parameters of the optimization problem. We propose a robust model for the bi-objective shortest path problem applied in a smart mobility context: Finding routes for cars in a city to minimize travel time and...

Because of their effectiveness and flexibility in finding useful solutions, Genetic Algorithms (GAs) are very popular search techniques for solving complex optimization problems in scientific and industrial fields. Parallel GAs (PGAs), and especially distributed ones have been usually presented as the way to overcome the time-consuming shortcoming...

The Next Release Problem consists in selecting a subset of requirements to develop in the next release of a software product. The selection should be done in a way that maximizes the satisfaction of the stakeholders while the development cost is minimized and the constraints of the requirements are fulfilled. Recent works have solved the problem us...

Finding an available parking space can be difficult in parts of most cities, especially in city centers. In this article, we address the study of parking occupancy data in Birmingham, Glasgow, Norfolk, and Nottingham in the United Kingdom. We test several prediction strategies, such as polynomial fitting, Fourier series, K-means clustering, and tim...

Having a mechanism to mathematically model the problem of the optimal allocation of parking spots within cities could bring great benefits to society. According to the International Parking Institute, about 38% of the cars circulating throughout a city are looking for available parking spots, leading to increased pollution and subsequent health pro...

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 fast demographic growth, together with the population concentration in cities and the increasing amount of daily waste, are factors that are pushing to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to optimally manage of the waste collection process, which represents 70% of the operational cost in...

In smart cities, when the real-time control of traffic lights is not possible, the global optimization of traffic-light programs (TLPs) requires the simulation of a traffic scenario (traffic flows across the whole city) that is estimated after collecting data from sensors at the street level. However, the highly dynamic traffic of a city means that...

This study presents a new technique based on Deep Learning with Recurrent Neural Networks to address the prediction of car park occupancy rate. This is an interesting problem in smart mobility and we here approach it in an innovative way, consisting in automatically design a deep network that encapsulates the behavior of the car occupancy and then...

Managing the waste collection service is a challenge in the fast-growing city context. A key to success in planning the collection is having an accurate prediction of the filling level of the waste containers. In this study we present a solution to the waste generation prediction problem based on recurrent neural networks. Particularly, we introduc...

Smart city initiatives have emerged to mitigate the negative effects of a very fast growth of urban areas. A number of universities are applying smart city solutions to face similar challenges in their campuses. In this study, we analyze the possibility of using low cost sensors based on detecting wireless signals of light commodity devices to trac...

This article develops the design, installation, exploitation, and final utilization of intelligent techniques, hardware, and software for understanding mobility in a modern city. We focus on a smart-campus initiative in the University of Malaga as the scenario for building this cyber–physical system at a low cost, and then present the details of a...

Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment due to prompt and elastic access to huge amounts of computing resources. Autoscalers are middleware-level software componen...

In this article we present a novel approach for calculating realistic traffic flows for traffic simulators, called Flow Generator Algorithm (FGA). We start with an original map from OpenStreetMap and traffic data collected at different measurement points, published by the city’s authorities, to produce a model consisting of the simulation map and a...

The efficiency of a software piece is a key factor for many systems. Real-time programs, critical software, device drivers, kernel OS functions and many other software pieces which are executed thousands or even millions of times per day require a very efficient execution. How this software is built can significantly affect the run time for these p...

Software frameworks are daily and extensively used in research, both for fundamental studies and applications. Researchers usually trust in the quality of these frameworks without any evidence that they are correctly build, indeed they could contain some defects that potentially could affect to thousands of already published and future papers. Cons...

This paper is a brief description of the revamped presentation based in the original one I had the honor to deliver back in 2009 during the very first SSBSE in London. At this time, the many international forces dealing with search, optimization, and learning (SOL) met software engineering (SE) researchers in person, all of them looking for a quant...

In this paper we present a methodology to develop self-* methods at a low computational cost. Instead of going purely ad-hoc we define several simple steps to include theoretical models as additional information in our algorithm. Our idea is to incorporate the predictive information (future behavior) provided by well-known mathematical models or ot...

Deep learning hyper-parameter optimization is a tough task. Finding an appropriate network configuration is a key to success, however most of the times this labor is roughly done. In this work we introduce a novel library to tackle this problem, the Deep Learning Optimization Library: DLOPT. We briefly describe its architecture and present a set of...

Deep learning hyper-parameter optimization is a tough task. Finding an appropriate network configuration is a key to success, however most of the times this labor is roughly done. In this work we introduce a novel library to tackle this problem, the Deep Learning Optimization Library: DLOPT. We briefly describe its architecture and present a set of...

A smart meter enables electric utilities to get detailed insights into their customer needs, allowing them to offer tailored products and services, and to succeed in an increasingly competitive market. While in an ideal world companies would treat every customer as an individual, in practice this is rather difficult. For this reason, companies usua...

Nowadays, city streets are populated not only by private vehicles but also by public transport, distribution of goods, and deliveries. Since each vehicle class has a maximum cargo capacity, we study in this article how authorities could improve the road traffic by changing the different vehicle proportions: sedans, minivans, full-size vans, trucks,...

The fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management of the waste collection process, which represents 70% of the operation...

This article proposes a mobility architecture, called Green Swarm, to reduce greenhouse gas emissions from road traffic in smart cities. The traffic flow optimization of four European cities: Malaga, Stockholm, Berlin, and Paris, is addressed with new case studies importing each city's actual roads and traffic lights from OpenStreetMap into the SUM...

Systolic Genetic Search (SGS) is a recently proposed optimization algorithm based on the circulation of solutions through a bidimensional grid of cells and the application of evolutionary operators within the cells to the moving solutions. Until now, the influence of the solutions flow on the results of SGS has only been empirically studied. In thi...

Recurrent neural networks are strong dynamic systems, but they are very sensitive to their hyper-parameter configuration. Moreover, training properly a recurrent neural network is a tough task, therefore selecting an appropriate configuration is critical. There have been proposed varied strategies to tackle this issue, however most of them are stil...

On mobile phones, users and developers use apps official marketplaces serving as repositories of apps. The Google Play Store and Apple Store are the official marketplaces of Android and Apple products which offer more than a million apps. Although both repositories offer description of apps, information concerning performance is not available. Due...

The constantly increasing number of vehicles and the immense size and complexity of road maps set a tough scenario for real world routing. In spite of the tremendous efforts done up to date to tackle this problem, finding the shortest-path in practice is still a challenge due to memory and time constrains. Moreover, most of the efforts have been fo...

Most of the world population lives in urban areas, and it is expected that the number of inhabitants in cities will be 75% of the world's population by 2050. Thus, a wide range of challenges have to be faced by the different city stakeholders in order to mitigate the negative effects of a very fast growth of such urban areas. One of the main concer...