Publications

@article{DBLP:journals/ijris/DahiMD17,

title = {A 0-1 bat algorithm for cellular network optimisation: a systematic 

 study on mapping techniques},

author = {Zakaria Abdelmoiz Dahi and 

 Chaker Mezioud and 

 Amer Draa},

url = {https://doi.org/10.1504/IJRIS.2017.10007147},

doi = {10.1504/IJRIS.2017.10007147},

year  = {2017},

date = {2017-01-01},

journal = {Int. J. Reason. based Intell. Syst.},

volume = {9},

number = {1},

pages = {22--42},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{296,

title = {A Mobile Application and Academic Portal to Support Professionals Working with People Having Severe Intellectual or Developmental Disabilities},

author = {Francisco Chicano and Gabriel Luque},

url = {http://dx.doi.org/10.1016/j.sbspro.2017.02.108},

doi = {10.1016/j.sbspro.2017.02.108},

issn = {18770428},

year  = {2017},

date = {2017-01-01},

journal = {Procedia - Social and Behavioral Sciences},

volume = {237},

pages = {568 - 575},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saborido_global_2017,

title = {Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front},

author = {Rubén Saborido and Ana B Ruiz and Mariano Luque},

url = {http://dx.doi.org/10.1162/EVCO_a_00175},

doi = {10.1162/EVCO_a_00175},

year  = {2017},

date = {2017-01-01},

journal = {Evolutionary Computation},

volume = {25},

number = {2},

pages = {309--349},

abstract = {In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA (global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{290,

title = {On the use of developerstextquoteright context for automatic refactoring of software anti-patterns},

author = {Rodrigo Morales and Z é and Foutse Khomh and Giuliano Antoniol and Francisco Chicano},

url = {http://www.sciencedirect.com/science/article/pii/S0164121216300632},

doi = {http://dx.doi.org/10.1016/j.jss.2016.05.042},

issn = {0164-1212},

year  = {2017},

date = {2017-06-01},

journal = {Journal of Systems and Software},

volume = {128},

pages = {236-251},

abstract = {
Abstract 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 advised to interleave refactoring activities with their regular coding tasks to remove anti-patterns, and consequently improve software design quality. However, because the number of anti-patterns in a software system can be very large, and their interactions can require a solution in a set of conflicting objectives, the process of manual refactoring can be overwhelming. To automate this process, previous works have modeled anti-patterns refactoring as a batch process where a program provides a solution for the total number of classes in a system, and the developer has to examine a long list of refactorings, which is not feasible in most situations. Moreover, these proposed solutions often require that developers modify classes on which they never worked before (i.e., classes on which they have little or no knowledge). To improve on these limitations, this paper proposes an automated refactoring approach, ReCon (Refactoring approach based on task Context), that leverages information about a developertextquoterights task (i.e., the list of code entities relevant to the developertextquoterights task) and metaheuristics techniques to compute the best sequence of refactorings that affects only entities in the developertextquoterights context. We mine 1705 task contexts (collected using the Eclipse plug-in Mylyn) and 1013 code snapshots from three open-source software projects (Mylyn, PDE, Eclipse Platform) to assess the performance of our proposed approach. Results show that ReCon can remove more than 50% of anti-patterns in a software system, using fewer resources than the traditional approaches from the literature.
},

keywords = {Metaheuristics},

pubstate = {published},

tppubtype = {article}

}

@article{298,

title = {Preface to the Special Issue on Theory of Genetic and Evolutionary Computation},

author = {Carola Doerr and Francisco Chicano},

url = {http://rdcu.be/F1tG},

doi = {10.1007/s00453-017-0280-x},

issn = {0178-4617},

year  = {2017},

date = {2017-01-01},

journal = {Algorithmica},

volume = {78},

pages = {558 - 560},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ruiz_preference-based_2017,

title = {Preference-based Evolutionary Multi-objective Optimization for Solving Fuzzy Portfolio Selection Problems},

author = {Ana B Ruiz and Rubén Saborido and José D Bermúdez and Mariano Luque and Enriqueta Vercher},

doi = {10.24309/recta.2017.18.1.01},

year  = {2017},

date = {2017-01-01},

journal = {Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA. Rect@},

volume = {18},

number = {1},

pages = {1--15},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/swevo/DahiMD16,

title = {A quantum-inspired genetic algorithm for solving the antenna positioning 

 problem},

author = {Zakaria Abdelmoiz Dahi and 

 Chaker Mezioud and 

 Amer Draa},

url = {https://doi.org/10.1016/j.swevo.2016.06.003},

doi = {10.1016/j.swevo.2016.06.003},

year  = {2016},

date = {2016-01-01},

journal = {Swarm Evol. Comput.},

volume = {31},

pages = {24--63},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{291,

title = {Editorial for the Special Issue on Combinatorial Optimization Problems},

author = {Francisco Chicano and Christian Blum and Gabriela Ochoa},

doi = {10.1162/EVCO_e_00192},

issn = {1063-6560},

year  = {2016},

date = {2016-12-01},

journal = {Evolutionary Computation Journal},

volume = {24},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saborido_evolutionary_2016,

title = {Evolutionary Multi-objective Optimization Algorithms for Fuzzy Portfolio Selection},

author = {Rubén Saborido and Ana B Ruiz and José D Bermúdez and Enriqueta Vercher and Mariano Luque},

url = {http://dx.doi.org/10.1016/j.asoc.2015.11.005},

doi = {10.1016/j.asoc.2015.11.005},

issn = {1568-4946},

year  = {2016},

date = {2016-01-01},

journal = {Appl. Soft Comput.},

volume = {39},

number = {C},

pages = {48--63},

keywords = {Evolutionary multi-objective optimization, LR-fuzzy numbers, Pareto optimal solutions, Portfolio selection, Possibility distributions},

pubstate = {published},

tppubtype = {article}

}

@article{289,

title = {Gray Box Optimization for Mk Landscapes (NK Landscapes and MAX-kSAT)},

author = {Darrell L Whitley and Francisco Chicano and Brian W Goldman},

url = {http://www.mitpressjournals.org/doi/10.1162/EVCO_a_00184},

doi = {10.1162/EVCO_a_00184},

issn = {1063-6560},

year  = {2016},

date = {2016-01-01},

journal = {Evolutionary Computation},

volume = {24},

pages = {491 - 519},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ferrer2016a,

title = {Intelligent testing of traffic light programs: Validation in smart mobility scenarios},

author = {Javier Ferrer and José García-Nieto and Enrique Alba and Francisco Chicano},

url = {http://www.hindawi.com/journals/mpe/2016/3871046/},

doi = {10.1155/2016/3871046},

issn = {15635147},

year  = {2016},

date = {2016-01-01},

journal = {Mathematical Problems in Engineering},

volume = {2016},

pages = {1--19},

abstract = {textcopyright 2016 Javier Ferrer et al. 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 citizens. In this paper, we propose a validation strategy based on genetic algorithms and feature models for the automatic generation of different traffic scenarios checking the robustness of traffic light cycle programs. We have concentrated on an extensive urban area in the city of Malaga (in Spain), in which we validate a set of candidate cycle programs generated by means of four optimization algorithms: Particle Swarm Optimization for Traffic Lights, Differential Evolution for Traffic Lights, random search, and Sumo Cycle Program Generator. We can test the cycles of traffic lights considering the different states of the city, weather, congestion, driver expertise, vehicle's features, and so forth, but prioritizing the most relevant scenarios among a large and varied set of them. The improvement achieved in solution quality is remarkable, especially for C O 2 emissions, in which we have obtained a reduction of 126.99% compared with the experts' solutions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/asc/DahiMD16,

title = {On the efficiency of the binary flower pollination algorithm: Application 

 on the antenna positioning problem},

author = {Zakaria Abdelmoiz Dahi and 

 Chaker Mezioud and 

 Amer Draa},

url = {https://doi.org/10.1016/j.asoc.2016.05.051},

doi = {10.1016/j.asoc.2016.05.051},

year  = {2016},

date = {2016-01-01},

journal = {Appl. Soft Comput.},

volume = {47},

pages = {395--414},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ruiz_combined_2015,

title = {A combined interactive procedure using preference-based evolutionary multiobjective optimization. Application to the efficiency improvement of the auxiliary services of power plants},

author = {Ana B Ruiz and Mariano Luque and Francisco Ruiz and Rubén Saborido},

url = {http://www.sciencedirect.com/science/article/pii/S0957417415003723},

doi = {http://dx.doi.org/10.1016/j.eswa.2015.05.036},

issn = {0957-4174},

year  = {2015},

date = {2015-01-01},

journal = {Expert Systems with Applications},

volume = {42},

number = {21},

pages = {7466 -- 7482},

abstract = {Abstract While the auxiliary services required for the operation of power plants are not the main components of the plant, their energy consumption is often significant, and it can be reduced by implementing a series of improvement strategies. However, the cost of implementing these changes can be very high, and has to be evaluated. Indeed, a further economic analysis should be considered in order to maximize the profitability of the investment. In this paper, we propose a multiobjective optimization problem to determine the most suitable strategies to maximize the energy saving, to minimize the economic investment and to maximize the Internal Rate of Return of the investment. Solving this real-life multiobjective optimization problem with a decision maker presents several challenges and difficulties and we have developed a novel interactive procedure which combines three different approaches in order to make use of the main advantages of each method. The idea is to start with the approximation of the Pareto optimal set, in order to gain a global understanding of the trade-offs among the objectives, using evolutionary multiobjective optimization; next step is aiding the decision maker to explore the efficient set and to identify the subset of solutions which fits her/his preferences, for which interactive multiple criteria decision making methodologies are used; and finally we concentrate the search for new solutions into the most interesting part of the efficient set with the help of a preference-based evolutionary algorithm. This allows us to build a flexible scheme that is progressively adapted to the decision maker’s reactions until (s)he finds the most preferred solution. The interactive combined procedure proposed is applied in practice for solving the problem of the auxiliary services with a real decision maker, extracting interesting insights about the efficiency improvement of the auxiliary services. With this practical application, we show the usefulness of the interactive procedure proposed, and we highlight the importance of an understandable feedback and an adaptive process.},

keywords = {Interactive procedure},

pubstate = {published},

tppubtype = {article}

}

@article{ruiz_preference-based_2015,

title = {A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm},

author = {Ana Belén Ruiz and Rubén Saborido and Mariano Luque},

url = {http://dx.doi.org/10.1007/s10898-014-0214-y},

doi = {10.1007/s10898-014-0214-y},

issn = {1573-2916},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Global Optimization},

volume = {62},

number = {1},

pages = {101--129},

abstract = {When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the region of interest of the Pareto optimal front. In this paper, we suggest a preference-based EMO algorithm called weighting achievement scalarizing function genetic algorithm (WASF-GA), which considers the preferences of the decision maker (DM) expressed by means of a reference point. The main purpose of WASF-GA is to approximate the region of interest of the Pareto optimal front determined by the reference point, which contains the Pareto optimal objective vectors that obey the preferences expressed by the DM in the best possible way. The proposed approach is based on the use of an achievement scalarizing function (ASF) and on the classification of the individuals into several fronts. At each generation of WASF-GA, this classification is done according to the values that each solution takes on the ASF for the reference point and using different weight vectors. These vectors of weights are selected so that the vectors formed by their inverse components constitute a well-distributed representation of the weight vectors space. The efficiency and usefulness of WASF-GA is shown in several test problems in comparison to other preference-based EMO algorithms. Regarding a metric based on the hypervolume, we can say that WASF-GA has outperformed the other algorithms considered in most of the problems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{181,

title = {Fitness Probability Distribution of Bit-Flip Mutation},

author = {Francisco Chicano and Andrew M Sutton and Darrell L Whitley and Enrique Alba},

url = {http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00130},

doi = {10.1162/EVCO_a_00130},

issn = {1063-6560},

year  = {2015},

date = {2015-01-01},

journal = {Evolutionary Computation},

volume = {23},

pages = {217-248},

chapter = {217},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saborido_impact_2015,

title = {On the impact of sampling frequency on software energy measurements.},

author = {Rubén Saborido and Venera Arnaoudova and Giovanni Beltrame and Foutse Khomh and Giuliano Antoniol},

url = {http://dblp.uni-trier.de/db/journals/peerjpre/peerjpre3.html#SaboridoABKA15},

year  = {2015},

date = {2015-01-01},

journal = {PeerJ PrePrints},

volume = {3},

pages = {e1219},

keywords = {dblp},

pubstate = {published},

tppubtype = {article}

}

@article{luque_use_2015,

title = {On the use of the $$L_p$$ L p distance in reference point-based approaches for multiobjective optimization},

author = {Mariano Luque and Ana B Ruiz and Rubén Saborido and Óscar D Marcenaro-Gutiérrez},

url = {http://dx.doi.org/10.1007/s10479-015-2008-0},

doi = {10.1007/s10479-015-2008-0},

issn = {1572-9338},

year  = {2015},

date = {2015-01-01},

journal = {Annals of Operations Research},

volume = {235},

number = {1},

pages = {559--579},

abstract = {Reference point-based methods are very useful techniques for solving multiobjective optimization problems. In these methods, the most commonly used achievement scalarizing functions are based on the Tchebychev distance (minmax approach), which generates every Pareto optimal solution in any multiobjective optimization problem, but does not allow compensation among the deviations to the reference values given that it minimizes the value of the highest deviation. At the same time, for any $$1 textbackslashbackslashle p textbackslashbackslashle textbackslashbackslashinfty $$ 1 ≤ p ≤ ∞ , compromise programming minimizes the $$L_p$$ L p distance to the ideal objective vector from the feasible objective region. Although the ideal objective vector can be replaced by a reference point, achievable reference points are not supported by this approach, and special care must be taken in the unachievable case. In this paper, for $$1 textbackslashbackslashle p textless textbackslashbackslashinfty $$ 1 ≤ p textless ∞ , we propose a new scheme based on the $$L_p$$ L p distance, in which different single-objective optimization problems are designed and solved depending on the achievability of the reference point. The formulation proposed allows different compensation degrees among the deviations to the reference values. It is proven that, in the achievable case, any optimal solution obtained is efficient, and, in the unachievable one, it is at least weakly efficient, although it is assured to be efficient if an augmentation term is added to the new formulation. Besides, we suggest an interactive algorithm where the new formulation is embedded. Finally, we show the empirical advantages of the new formulation by its application to both numerical problems and a real multiobjective optimization problem, for achievable and unachievable reference points.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ferrer2014,

title = {Search based algorithms for test sequence generation in functional testing},

author = {Javier Ferrer and Peter M Kruse and Francisco Chicano and Enrique Alba},

url = {http://www.sciencedirect.com/science/article/pii/S0950584914001827},

doi = {10.1016/j.infsof.2014.07.014},

issn = {09505849},

year  = {2015},

date = {2015-01-01},

journal = {Information and Software Technology},

volume = {58},

pages = {419--432},

abstract = {Context: The generation of dynamic test sequences from a formal specification, complementing traditional testing methods in order to find errors in the source code. Objective: In this paper we extend one specific combinatorial test approach, the Classification Tree Method (CTM), with transition information to generate test sequences. Although we use CTM, this extension is also possible for any combinatorial testing method. Method: The generation of minimal test sequences that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search-based approaches are required to find such (near) optimal test sequences. Results: The experimental analysis compares the search-based technique with a greedy algorithm on a set of 12 hierarchical concurrent models of programs extracted from the literature. Our proposed search-based approaches (GTSG and ACOts) are able to generate test sequences by finding the shortest valid path to achieve full class (state) and transition coverage. Conclusion: The extended classification tree is useful for generating of test sequences. Moreover, the experimental analysis reveals that our search-based approaches are better than the greedy deterministic approach, especially in the most complex instances. All presented algorithms are actually integrated into a professional tool for functional testing.},

keywords = {Ant Colony Optimization, Classification Tree Method, functional, Functional testing, Genetic Algorithm, search based software engineering, sequence, Test sequence generation},

pubstate = {published},

tppubtype = {article}

}

@article{223,

title = {Search Based Software Engineering (SBSE)},

author = {Mark Harman and Francisco Chicano},

url = {http://dx.doi.org/10.1016/j.jss.2015.01.051},

doi = {10.1016/j.jss.2015.01.051},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Systems and Software},

volume = {103},

pages = {266},

keywords = {},

pubstate = {published},

tppubtype = {article}

}