Francisco Chicano

• Ph. D.
• Professor (Associate) at University of Malaga

Quantum computers leverage the principles of quantum mechanics to do computation with a potential advantage over classical computers. While a single classical computer transforms one particular binary input into an output after applying one operator to the input, a quantum computer can apply the operator to a superposition of binary strings to prov...

In pseudo-Boolean optimization, a variable interaction graph represents variables as vertices, and interactions between pairs of variables as edges. In black-box optimization, the variable interaction graph may be at least partially discovered by using empirical linkage learning techniques. These methods never report false variable interactions, bu...

Gray-box optimization leverages the information available about the mathematical structure of an optimization problem to design efficient search operators. Efficient hill climbers and crossover operators have been proposed in the domain of pseudo-Boolean optimization and also in some permutation problems. However, there is no general rule on how to...

We define a new multi-objective optimization problem that consists in assigning migrants to different geographical subareas and, optionally, providing a job contract to them trying to maximize the satisfaction of the migrants and cover the labor needs of the different subareas. We provide an integer linear program for the problem and prove that the...

We define a new multi-objective optimization problem that consists in assigning migrants to different geographical subareas and, optionally, providing a job contract to them trying to maximize the satisfaction of the migrants and cover the labor needs of the different subareas. We provide an integer linear program for the problem and prove that the...

Gate-based quantum computation describes algorithms as quantum circuits. These can be seen as a set of quantum gates acting on a set of qubits. To be executable, the circuit requires complex transformations to comply with the physical constraints of the machines. This process is known as transpilation, where qubits’ layout initialisation is one of...

In pseudo-Boolean optimization, a variable interaction graph represents variables as vertices, and interactions between pairs of variables as edges. In black-box optimization, the variable interaction graph may be at least partially discovered by using empirical linkage learning techniques. These methods never report false variable interactions, bu...

A new construction is introduced for creating random MAX-3SAT instances with low nonlinearity. Instead of generating random clauses, we generate random SAT expressions over 3 variables and then convert these into CNF SAT clauses. We prove that this yields structured problems with much lower nonlinearity. We also introduce a new method for weighting...

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...

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...

Inspired by the recent 11th Global Trajectory Optimisation Competition, this paper presents the asteroid routing problem (ARP) as a realistic benchmark of algorithms for expensive bound-constrained black-box optimization in permutation space. Given a set of asteroids’ orbits and a departure epoch, the goal of the ARP is to find the optimal sequence...

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...

Inspired by the recent 11th Global Trajectory Optimisation Competition, this paper presents the asteroid routing problem (ARP) as a realistic benchmark of algorithms for expensive bound-constrained black-box optimization in permutation space. Given a set of asteroids' orbits and a departure epoch, the goal of the ARP is to find the optimal sequence...

Mutation testing is a well-established but costly technique to assess and improve the fault detection ability of test suites. This technique consists of introducing subtle changes in the code of a program, which are expected to be detected by the designed test cases. Among the strategies conceived to reduce its cost, evolutionary mutation testing (...

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...

An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions....

The 2021 Genetic and Evolutionary Computation Conference (GECCO 2021) was planned to be held in hybrid mode in Lille, France, on July 10th-14th, 2021. After a careful analysis of the evolution of the pandemic and the results of a poll to frequent attendees on the preferred form of attendance, a decision was made in mid-December to run GECCO'21 in o...

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...

The 2021 Genetic and Evolutionary Computation Conference (GECCO 2021) was planned to be held in hybrid mode in Lille, France, on July 10th-14th, 2021. After a careful analysis of the evolution of the pandemic and the results of a poll to frequent attendees on the preferred form of attendance, a decision was made in mid-December to run GECCO'21 in o...

In contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms. It is believed that this is the consequence of the non-random structure of the distribution of variables into clauses. In order to produce benchmark instances resembling those of real-world formulas with a given struc...

In Software Product Lines, it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist (Ferrer et al. in: Squillero, Sim (eds) EvoApps 2017, LNCS 10200, Springer, The Netherlands, pp 3–19, 2017). Thus, we want to find a minimal subset of the product family th...

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 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...

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...

It is our pleasure to introduce this special issue on the recent advances in the theoretical foundations of evolutionary computation (EC). While in the early days of this field, theoretical analyses inevitably focused on simplified models of evolutionary algorithms (EAs), the continuous progress made in the development of suitable mathematical tech...

We revisit the fitness landscape structure of random MAX-SAT instances, and address the question: what structural features change when we go from easy underconstrained instances to hard overconstrained ones? Some standard techniques such as autocorrelation analysis fail to explain what makes instances hard to solve for stochastic local search algor...

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...

Since its appearance in 2001, search-based software engineering has allowed software engineers to use optimisation techniques to automate distinctive human problems related to software management and development. The scientific community in Spain has not been alien to these advances. Their contributions cover both the optimisation of software engin...

One of the most popular issues that we can find in cities is transportation problems: traffic jams, pollution and the transportation cost fees. The concept of taxi sharing is considered as a promising idea to reduce some of the transportation problems. A group of people travels from the same origin to different destinations. Our goal is to assign t...

Context: Mutation testing is considered to be a powerful approach to assess and improve the quality of test suites. However, this technique is expensive mainly because some mutants are semantically equivalent to the original program; in general, equivalent mutants require manual revision to differentiate them from useful ones, which is known as the...

Local Optima Networks (LONs) are a valuable tool to understand fitness landscapes of optimization problems observed from the perspective of a search algorithm. Local optima of the optimization problem are linked by an edge in LONs when an operation in the search algorithm allows one of them to be reached from the other. Previous work analyzed sever...

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...

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...

The output of an optimal recombination operator for two parent solutions is a solution with the best possible value for the objective function among all the solutions fulfilling the gene transmission property: the value of any variable in the offspring must be inherited from one of the parents. This set of solutions coincides with the largest dynas...

Anti-patterns are poor solutions to design problems that make software systems hard to understand and to extend. Components involved in anti-patterns are reported to be consistently related to high changes and faults rates. Developers are advised to perform refactoring to remove anti-patterns, and consequently improve software design quality and re...

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,...

Partition Crossover is a recombination operator for pseudo-Boolean optimization with the ability to explore an exponential number of solutions in linear or square time. It decomposes the objective function as a sum of subfunctions, each one depending on a different set of variables. The decomposition makes it possible to select the best parent for...

There are two important challenges for local search algorithms when applied to Maximal Satisfiability (MAXSAT). 1) Local search spends a great deal of time blindly exploring plateaus in the search space and 2) local search is less effective on application instances. This second problem may be related to local search's inability to exploit problem s...

Ouni et al. “Maintainability defects detection and correction: a multi-objective approach” proposed a search-based approach for generating optimal refactoring sequences. They estimated the size of the search space for the refactoring scheduling problem using a formulation that is incorrect; the search space is estimated to be too much larger than i...

With millions of smartphones sold every year, the development of mobile apps has grown substantially. The battery power limitation of mobile devices has push developers and researchers to search for methods to improve the energy efficiency of mobile apps. We propose a multiobjective refactoring approach to automatically improve the architecture of...

The NK hybrid genetic algorithm for clustering is proposed in this paper. In order to evaluate the solutions, the hybrid algorithm uses the NK clustering validation criterion 2 (NKCV2). NKCV2 uses information about the disposition of N small groups of objects. Each group is composed of K+1 objects of the dataset. Experimental results show that dens...

The Vehicle Routing Problem is a combinatorial problem with considerable industrial applications such as in traditional logistics and transportation, or in modern carpooling. The importance of even small contributions to this problem is strongly reflected in a significant cost savings, pollution, waste, etc., given the high impact of the sector in...

The energy consumption of mobile apps is a trending topic and researchers are actively investigating the role of coding practices on energy consumption. Recent studies suggest that design choices can conflict with energy consumption. Therefore, it is important to take into account energy consumption when evolving the design of a mobile app. In this...

In gray-box optimization, the search algorithms have access to the variable interaction graph (VIG) of the optimization problem. For Mk Landscapes (and NK Landscapes) we can use the VIG to identify an improving solution in the Hamming neighborhood in constant time. In addition, using the VIG, deterministic Partition Crossover is able to explore an...

Resumen Las métricas CK a nivel de diseño orientado a objetos, son las que alcanzan un mayor consenso sobre la identificación de la necesidad de una refactorización. Para estimar el impacto de estas métricas de calidad en la refactorización en este trabajo nos basamos en la reducción de la entropía. Para ellos se parte de los datos validados de ref...

Road journeys are one of our most frequent daily tasks. Despite we need them, these trips have some associated costs: time, money, pollution, etc. One of the usual ways of modeling the road network is as a graph. The shortest path problem consists in finding the path in a graph that minimizes a certain cost function. However, in real world applicat...

In Software Product Lines (SPLs) it is not possible, in general, to test all products of the family. The number of products denoted by a SPL is very high due to the combinatorial explosion of features. For this reason, some coverage criteria have been proposed which try to test at least all feature interactions without the necessity to test all pro...

In combinatorial optimization, the goal is to find an optimal solution, according to some objective function, from a discrete search space. These problems arise widely in industry and academia and, unfortunately, many of them are NP-hard and no polynomial time algorithm can guarantee their solution to a certified optimality unless P=NP. Therefore,...

Some recent works claim that more support is required to include people with severe intellectual or developmental disabilities in their communities. This support can be provided in the form of tools for the professionals that will be in charge of the disabled people, that is, teachers, psychologists, speech therapists, etc. In this communication, w...

This book constitutes the proceedings of the second International Conference on Smart Cities, Smart-CT 2017, held in Málaga, Spain, in June 2017. The 16 papers presented in this volume were carefully reviewed and selected from 21 submissions. The topics covered include studies and tools to improve road traffic, energy consumption, logistics, framew...

The boom in mobile apps has changed the traditional landscape of software development by introducing new challenges due to the limited resources of mobile devices, e.g., memory, CPU, network bandwidth and battery. The energy consumption of mobile apps is nowadays a hot topic and researchers are actively investigating the role of coding practices on...

Resumen El problema de selección de requisitos (o Next Release Problem , NRP) consiste en seleccionar el subconjunto de requisitos que se va a desarrollar en la siguiente versión de una aplicación software. Esta selección se debe hacer de tal forma que maximice la satisfacción de las partes interesadas a la vez que se minimiza el esfuerzo empleado...

The use of good evaluation functions is essential when evolutionary algorithms are employed for clustering. The NK internal clustering validation measure is proposed for hard partitional clustering. The evaluation function is composed of N subfunctions, where N is the number of objects in the dataset. Each subfunction is influenced by a group of K+...

Efficient hill climbers have been recently proposed for single- and multi-objective pseudo-Boolean optimization problems. For k-bounded pseudo-Boolean functions where each variable appears in at most a constant number of subfunctions, it has been theoretically proven that the neighborhood of a solution can be explored in constant time. These hill c...

Road transportation is becoming a major concern in modern cities. The growth of the number of vehicles is provoking an important increment of pollution and greenhouse gas emissions generated by road traffic. In this paper, we present CTPATH, an innovative smart mobility software system that offers efficient paths to drivers in terms of travel time...

Given a group of people traveling from the same origin to multiple destinations, the Taxi Sharing Problem consists in assigning taxis to each person such that the total cost spent by the group of people is minimized. This problem arises in the context of Smart Mobility, where the resources of a city must be optimized to save costs and pollution whi...

Anti-patterns are poor solutions to design problems that make software systems hard to understand and extend. Entities involved in anti-patterns are reported to be consistently related to high change and fault rates. Refactorings, which are behavior preserving changes are often performed to remove anti-patterns from software systems. Developers are...

This paper investigates Gray Box Optimization for pseudo-Boolean optimization problems composed of M subfunctions, where each subfunction accepts at most k variables. We will refer to these as Mk Landscapes. In Gray Box Optimization, the optimizer is given access to the set of M subfunctions. We prove Gray Box Optimization can efficiently compute h...

Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand-alone search method, but it can also be hybridized with evolutionary algorithms. Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in co...

Anti-patterns are poor design choices that hinder code evolution, and understandability. Practitioners perform refactoring, that are semantic-preserving-code transformations, to correct anti-patterns and to improve design quality. However, manual refactoring is a consuming task and a heavy burden for developers who have to struggle to complete thei...

Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand along search methods, but it can also be hybridized with evolutionary algorithms. Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in c...

Because of economical, technological and marketing reasons today’s software systems are more frequently being built as families where each product variant implements a different combination of features. Software families are commonly called Software Product Lines (SPLs) and over the past three decades have been the subject of extensive research and...

In smart cities, the use of intelligent automatic techniques to find efficient cycle programs of traffic lights is becoming an innovative front for traffic flow management. However, this automatic programming of traffic lights requires a validation process of the generated solutions, since they can affect the mobility (and security) of millions of...

This book constitutes the proceedings of the First International Conference on Smart Cities, Smart-CT 2016, held in Malaga, Spain, in June 2016. The 16 papers presented in this volume were carefully reviewed and selected from 28 submissions. They topics covered include studies and tools to improve road traffic, energy consumption, logistics, frame...

Resumen El problema de selección de requisitos (o Next Release Problem , NRP) consiste en seleccionar el subconjunto de requisitos que se va a desarrollar en la siguiente versión de una aplicación software. Esta selección se debe hacer de tal forma que maximice la satisfacción de las partes interesadas a la vez que se minimiza el esfuerzo empleado...

Local optima networks are a recent model of fitness landscapes. They compress the landscape by representing local optima as nodes, and search transitions among them as edges. Previous local optima networks considered transitions based on mutation; this study looks instead at transitions based on deterministic recombination. We define and analyse ne...

This paper introduces the NK Echo State Network. The problem of learning in the NK Echo State Network is reduced to the problem of optimizing a special form of a Spin Glass Problem known as an NK Landscape. No weight adjustment is used; all learning is accomplished by spinning up (turning on) or spinning down (turning off) neurons in order to find...

En este traba jo se presenta una herramienta software, denominada Interactive SPS o iSPS, que permite resolver, de forma interactiva, instancias del problema de Planificación de Proyectos Software (SPS) haciendo uso de algoritmos evolutivos basados en punto de referencia. La finalidad de la herramienta es ayudar al director de proyectos software en...

A partition crossover operator is introduced for use with NK landscapes, MAX-kSAT and for all k-bounded pseudo-Boolean functions. By definition, these problems use a bit representation. Under partition crossover, the evaluation of offspring can be directly obtained from partial evaluations of substrings found in the parents. Partition crossover exp...