Fourier Transform-based Surrogates for Permutation Problems

In the context of pseudo-Boolean optimization, surrogate functions based on the Walsh-Hadamard transform have been recently proposed with great success. It has been shown that lower-order components of the Walsh-Hadamard transform have usually a larger influence on the value of the objective function. Thus, creating a surrogate model using the lower-order components of the transform can provide a good approximation to the objective function. The Walsh-Hadamard transform in pseudo-Boolean optimization is a particularization in the binary representation of a Fourier transform over a finite group, precisely defined in the framework of group representation theory. Using this more general definition, it is possible to define a Fourier transform for the functions over permutations. We propose in this paper the use of surrogate functions based on the Fourier transforms over the permutation space. We check how similar the proposed surrogate models are to the original objective function and we also apply regression to learn a surrogate model based on the Fourier transform. The experimental setting includes two permutation problems for which the exact Fourier transform is unknown based on the problem parameters: the Asteroid Routing Problem and the Single Machine Total Weighted Tardiness.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Ministerio de Ciencia, Innovación y Universidades del Gobierno de España under grants PID 2020-116727RB-I00 and PRX21/00669, and by EU Horizon 2020 research and innovative program (grant 952215, TAILOR ICT-48 network). Thanks to the Supercomputing and Bioinnovation Center (SCBI) of Universidad de Málaga for their provision of computational resources and support

Estimating Software Testing Complexity

Context: Complexity measures provide us some information about software artifacts. A measure of the difficulty of testing a piece of code could be very useful to take control about the test phase. Objective: The aim in this paper is the definition of a new measure of the difficulty for a computer to gen erate test cases, we call it Branch Coverage Expectation (BCE). We also analyze the most common com plexity measures and the most important features of a program. With this analysis we are trying to discover whether there exists a relationship between them and the code coverage of an automatically generated test suite. Method: The definition of this measure is based on a Markov model of the program. This model is used not only to compute the BCE, but also to provide an estimation of the number of test cases needed to reach a given coverage level in the program. In order to check our proposal, we perform a theoretical val idation and we carry out an empirical validation study using 2600 test programs. Results: The results show that the previously existing measures are not so useful to estimate the difficulty of testing a program, because they are not highly correlated with the code coverage. Our proposed mea sure is much more correlated with the code coverage than the existing complexity measures. Conclusion: The high correlation of our measure with the code coverage suggests that the BCE measure is a very promising way of measuring the difficulty to automatically test a program. Our proposed measure is useful for predicting the behavior of an automatic test case generator.This work has been partially funded by the Spanish Ministry of Science and Innovation and FEDER under contract TIN2011-28194 (the roadME project

Autoevaluación y evaluación entre iguales en una asignatura de redes de ordenadores

Con la llegada del EEES se ha fomentado el uso de la evaluación continuada como un ingrediente de la docencia de calidad que permite al alumno mantenerse puntualmente informado de su progreso en el proceso de aprendizaje. Este trabajo se centra en el uso de la autoevaluación y la evaluación entre iguales como base para conseguir esta evaluación continuada. En él detallamos la experiencia, durante los cursos 2008/09 y 2009/10, del uso de estas dos herramientas de aprendizaje en el contexto de una asignatura de redes de ordenadores de tercer curso de Ingeniería Técnica en Informática de Gestión.SUMMARY: The use of continuous assessment has been encouraged in the adaptation to the European Higher Education Area. It is considered an ingredient to the high quality teaching that allows the student to have a fast feedback of her/his learning progress. This work focuses in the use of self assessment and peer assessment as the basis for a continuous evaluation system. We detail our experience during the years 2008/09 and 2009/10 on using such teaching tools in the context of a subject on computer networks.Peer Reviewe

CMSA algorithm for solving the prioritized pairwise test data generation problem in software product lines.

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 that allows us to test all these possible combinations (pairwise). Furthermore, when testing a single product is a great effort, it is desirable to first test products composed of a set of priority features. This problem is called Prioritized Pairwise Test Data Generation Problem. State-of-the-art algorithms based on Integer Linear Programming for this problem are faster enough for small and medium instances. However, there exists some real instances that are too large to be computed with these algorithms in a reasonable time because of the exponential growth of the number of candidate solutions. Also, these heuristics not always lead us to the best solutions. In this work we propose a new approach based on a hybrid metaheuristic algorithm called Construct, Merge, Solve & Adapt. We compare this matheuristic with four algorithms: a Hybrid algorithm based on Integer Linear Programming, a Hybrid algorithm based on Integer Nonlinear Programming, the Parallel Prioritized Genetic Solver, and a greedy algorithm called prioritized-ICPL. The analysis reveals that CMSA is statistically significantly better in terms of quality of solutions in most of the instances and for most levels of weighted coverage, although it requires more execution time.This research has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (FEDER) under contract TIN2017-88213-R (6city project), the University of Málaga, Consejerıa de Economıa y Conocimiento de la Junta de Andalucıa and FEDER under contract UMA18-FEDERJA-003 (PRECOG project), the Ministry of Science, Innovation and Universities and FEDER under contract RTC-2017-6714-5 (ECOIoT project), the H2020 European Project Tailor (H2020-ICT-2019-3), the Spanish SBSE Research Network (RED2018-102472-T), and the University of Málaga under contract PPIT-UMA-B1-2017/07 (EXHAURO Project). J. Ferrer thanks University of Mállaga for his postdoc fellowship

Using metaheuristics for the location of bicycle stations

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 distance between a set of clients (citizens) and their closest facility (bike station). We have used a genetic algorithm, iterated local search, particle swarm optimization, simulated annealing, and variable neighbourhood search, to find the best locations for the bicycle stations and study their comparative advantages. We use irace to parameterize each algorithm automatically, to contribute with a methodology to fine-tune algorithms automatically. We have also studied different real data (distance and weights) from diverse open data sources from a real city, Malaga (Spain), hopefully leading to a final smart city application. We have compared our results with the implemented solution in Malaga. Finally, we have analyzed how we can use our proposal to improve the existing system in the city by adding more stations.This research was partially funded by the University of Málaga, Andalucía Tech, the Spanish MINECO and FEDER projects: TIN2014-57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

Generalizing and Unifying Gray-box Combinatorial Optimization Operators.

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 design these efficient operators in different representation domains. This paper proposes a general framework that encompasses all known gray-box operators for combinatorial optimization problems. The framework is general enough to shed light on the design of new efficient operators for new problems and representation domains. We also unify the proofs of efficiency for gray-box hill climbers and crossovers and show that the mathematical property explaining the speed-up of gray-box crossover operators, also explains the efficient identification of improving moves in gray-box hill climbers. We illustrate the power of the new framework by proposing an efficient hill climber and crossover for two related permutation problems: the Linear Ordering Problem and the Single Machine Total Weighted Tardiness Problem.This research is partially funded by project PID 2020-116727RB- I00 (HUmove) funded by MCIN/AEI/ 10.13039/501100011033; TAILOR ICT-48 Network (No 952215) funded by EU Horizon 2020 research and innovation programme; Junta de Andalucia, Spain, under contract QUAL21 010UMA; and the University of Malaga (PAR 4/2023). This work is also partially funded by a National Science Foundation (NSF) grant to D. Whitley, Award Number: 1908866

Validación Inteligente para la Sincronización de Semáforos Basada en Feature Models

El concepto de Smart City o Ciudad Inteligente engloba el conjunto de acciones y servicios, basados en las tecnologías de información y comunicación, que se ofrecen en un núcleo urbano. En este sentido, el uso de técnicas bioinspiradas para la gestión del flujo del tráfico mediante la sincronización de semáforos podría constituir uno de los aspectos más innovadores en los entornos urbanos en el futuro. No obstante, la programación automática de semáforos requiere además de un proceso de validación de las soluciones generadas, dado que afectan a la seguridad de miles de usuarios. En este trabajo se propone una estrategia de validación basada en los Modelos de Características (Feature Models) para generar automáticamente diversos escenarios para la comprobación de la robustez de los programas de semáforos. Como caso de estudio, se realiza la validación de programas de semáforos en el área urbana de la ciudad de Málaga, generados mediante cuatro algoritmos de optimización (PSO, DE, Random Search y SCPG). El resultado es información validada sobre el programa de semáforos que mejor actúa para un mayor porcentaje de situaciones de tráfico diferentes.Ministerio de Economía y Competitividad y FEDER: TIN2011-28194, BES-2012-055967 y BES-2009-01876

NK Hybrid Genetic Algorithm for Clustering

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 density-based regions can be identified by using NKCV2 with fixed small K. In NKCV2, the relationship between decision variables is known, which in turn allows us to apply gray box optimization. Mutation operators, a partition crossover, and a local search strategy are proposed, all using information about the relationship between decision variables. In partition crossover, the evaluation function is decomposed into q independent components; partition crossover then deterministically returns the best among 2^q possible offspring with computational complexity O(N). The NK hybrid genetic algorithm allows the detection of clusters with arbitrary shapes and the automatic estimation of the number of clusters. In the experiments, the NK hybrid genetic algorithm produced very good results when compared to another genetic algorithm approach and to state-of-art clustering algorithms.In Brazil, this research was partially funded by FAPESP (2015/06462-1, 2015/50122-0, and 2013/07375-0) and CNPq (303012/2015-3 and 304400/2014-9). In Spain, this research was partially funded by Ministerio de Economía y Competitividad (TIN2014-57341-R and TIN2017-88213-R) and by Ministerio de Educación Cultura y Deporte (CAS12/00274)

An Efficient QAOA via a Polynomial QPU-Needless Approach.

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum algorithm described as ansatzes that represent both the problem and the mixer Hamiltonians. Both are parameterizable unitary transformations executed on a quantum machine/simulator and whose parameters are iteratively optimized using a classical device to optimize the problem’s expectation value. To do so, in each QAOA iteration, most of the literature uses a quantum machine/simulator to measure the QAOA outcomes. However, this poses a severe bottleneck considering that quantum machines are hardly constrained (e.g. long queuing, limited qubits, etc.), likewise, quantum simulation also induces exponentially-increasing memory usage when dealing with large problems requiring more qubits. These limitations make today’s QAOA implementation impractical since it is hard to obtain good solutions with a reasonably-acceptable time/resources. Considering these facts, this work presents a new approach with two main contributions, including (I) removing the need for accessing quantum devices or large-sized classical machines during the QAOA optimization phase, and (II) ensuring that when dealing with some -bounded pseudo-Boolean problems, optimizing the exact problem’s expectation value can be done in polynomial time using a classical computer.This work is partially funded by Universidad de Málaga, Ministerio de Ciencia, Innovación y Universidades del Gobierno de España under grants PID 2020-116727RB-I00 (funded by MCIN/AEI/ 10.13039/501100011033) and PRX21/00669; and TAILOR ICT-48 Net- work (No 952215) funded by EU Horizon 2020 research and innovation programme. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics plus Integer Linear Programming.

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 the efficient solutions of the problem. In this work we combine these two ideas to propose a hybrid algorithm mixing an exploration heuristic for multiobjective optimization with integer linear programming to solve multiobjective problems with binary variables and linear constraints. The algorithm has been designed to provide an approximation of the Pareto front that is well-spread throughout the objective space. In order to check the performance, we compare it with three popular metaheuristics using two benchmarks of multiobjective binary constrained problems. The results show that the proposed approach provides better performance than the baseline algorithms in terms of number of the solutions, hypervolume, generational distance, inverted generational distance, and the additive epsilon indicator.This research is partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2017-88213-R (6city); Universidad de Málaga, Consejería de Economía y Conocimiento de la Junta de Andaluía and FEDER under grant number UMA18-FEDERJA-003 (PRECOG); Spanish Ministry of Science, Innovation and Universities and FEDER under contracts RTC-2017-6714-5 (Eco-IoT) and RED2018-102472-T (SEBASENet 2.0); and TAILOR ICT-48 Network (No 952215) funded by EU Horizon 2020 research and innovation programme. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech