Publications

@article{Ferrer2021,

title = {CMSA algorithm for solving the prioritized pairwise test data generation problem in software product lines},

author = {Javier Ferrer and Francisco Chicano and José Antonio Ortega-Toro},

url = {http://link.springer.com/10.1007/s10732-020-09462-w},

doi = {10.1007/s10732-020-09462-w},

issn = {1381-1231},

year  = {2021},

date = {2021-04-01},

journal = {Journal of Heuristics},

volume = {27},

number = {1-2},

pages = {229--249},

abstract = {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.},

keywords = {CMSA, Combinatorial optimization, Feature Models, Hybrid algorithms, Integer programming, Matheuristics, Software Product Lines},

pubstate = {published},

tppubtype = {article}

}

@article{Ramirez2020,

title = {A systematic literature review of the SBSE research community in Spain},

author = {Aurora Ramírez and Pedro Delgado-Pérez and Javier Ferrer and José Raúl Romero and Inmaculada Medina-Bulo and Francisco Chicano},

doi = {10.1007/s13748-020-00205-3},

issn = {21926360},

year  = {2020},

date = {2020-01-01},

journal = {Progress in Artificial Intelligence},

volume = {9},

number = {2},

pages = {113--128},

abstract = {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 engineering tasks and the proposal of new search algorithms. This review compiles the research efforts of this community in the area. With this aim, we propose a protocol to describe the review process, including the search sources, inclusion and exclusion criteria of candidate papers, the data extraction procedure and the categorisation of primary studies. After retrieving more than 3700 papers, 232 primary studies have been selected, whose analysis gives a precise picture of the current research state of the community, trends and future challenges. With 145 authors from 19 distinct institutions, results show that a diversity of tasks, including software planning, requirements, design and testing, and a large variety of techniques has been used, from exact search to evolutionary computation and swarm intelligence. Further, since 2015, specific scientific events have helped to bring together the community, improving collaborations, financial funding and internationalisation.},

keywords = {Research trends, Search-based Software Engineering, Spanish community, Systematic review},

pubstate = {published},

tppubtype = {article}

}

@article{luque_adaptive_2020,

title = {Adaptive Global WASF-GA to handle many-objective optimization problems},

author = {Mariano Luque and Sandra Gonzalez-Gallardo and Rubén Saborido and Ana B Ruiz},

url = {http://www.sciencedirect.com/science/article/pii/S2210650218306187},

doi = {https://doi.org/10.1016/j.swevo.2020.100644},

issn = {2210-6502},

year  = {2020},

date = {2020-05-01},

journal = {Swarm and Evolutionary Computation},

volume = {54},

number = {100644},

abstract = {In this paper, a new version of the aggregation-based evolutionary algorithm Global WASF-GA (GWASF-GA) for many-objective optimization is proposed, called Adaptive Global WASF-GA (A-GWASF-GA). The fitness function of GWASF-GA is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, which considers two reference points (the nadir and utopian points) and a set of weight vectors. Despite of the benefits of using these two reference points simultaneously and a well-distributed set of weight vectors, it is necessary to go a step further to get better approximations in problems with complicated Pareto optimal fronts. For this, in A-GWASF-GA, some of the weight vectors are re-calculated during the optimization process based on the sparsity of the solutions found so far, and taking into account some theoretical results demonstrated in this paper regarding the ASF considered. Different strategies are carried out to accelerate the convergence and to maintain the diversity. The computational results, carried out in comparison with RVEA, NSGA-III, and different versions of MOEA/D, show the potential of A-GWASF-GA in well-known but also in novel many-objective optimization benchmark problems.},

keywords = {Achievement scalarizing function, evolutionary algorithm, Many-objective optimization, Pareto optimal solutions, Weight vectors},

pubstate = {published},

tppubtype = {article}

}

@article{324,

title = {An experimental and practical study on the equivalent mutant connection: An evolutionary approach},

author = {Pedro Delgado-Pérez and Francisco Chicano},

url = {https://authors.elsevier.com/a/1b0bL3O8rCWDoI},

doi = {10.1016/j.infsof.2020.106317},

issn = {09505849},

year  = {2020},

date = {2020-01-01},

journal = {Information and Software Technology},

volume = {124},

pages = {106317},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{328,

title = {Evaluation of alternative design choices for evolutionary mutation testing by means of automated configuration},

author = {Pedro Delgado-Pérez and Francisco Chicano},

url = {http://link.springer.com/10.1007/s11219-020-09530-1},

doi = {10.1007/s11219-020-09530-1},

issn = {0963-9314},

year  = {2020},

date = {2020-06-01},

journal = {Software Quality Journal},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{morales_guest_2020,

title = {Guest Editorial Special Issue on Software Engineering Research and Practices for the Internet of Things},

author = {Rodrigo Morales and Rubén Saborido and S R Humayoun and Yann-Gaël Guéhéneuc},

doi = {10.1109/JIOT.2020.3026565},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Internet of Things Journal},

volume = {7},

number = {10},

pages = {10089--10090},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{morales_momit_2020,

title = {MoMIT: Porting a JavaScript Interpreter on a Quarter Coin},

author = {Rodrigo Morales and Rubén Saborido and Yann-Gaël Guéhéneuc},

doi = {10.1109/TSE.2020.2968061},

issn = {0098-5589},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Transactions on Software Engineering},

pages = {1--1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saborido_software_2020b,

title = {Software systems from smart city vendors},

author = {Rubén Saborido and Enrique Alba},

url = {http://www.sciencedirect.com/science/article/pii/S0264275119311539},

doi = {https://doi.org/10.1016/j.cities.2020.102690},

issn = {0264-2751},

year  = {2020},

date = {2020-01-01},

journal = {Cities},

volume = {101},

number = {102690},

abstract = {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 development. Smart city vendors provide software systems to support application developers, city managers, urban planners, and policy-makers in designing, implementing, deploying, and managing smart city applications for an effective urban development and management. Although there exist a variety of smart city software systems, their benefits and limitations are still unknown. In this paper we review smart city vendors and we provide insight into their software systems for smart cities. Thus, we come up with a good entry point, and here survey a large amount of information to help developers, city managers, urban planners, and policy-makers to take better decisions when choosing smart city software systems.},

keywords = {Industry, Smart city, Software},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/soco/DahiA20,

title = {The grid-to-neighbourhood relationship in cellular GAs: from design 

 to solving complex problems},

author = {Zakaria Abdelmoiz Dahi and 

 Enrique Alba},

url = {https://doi.org/10.1007/s00500-019-04125-w},

doi = {10.1007/s00500-019-04125-w},

year  = {2020},

date = {2020-01-01},

journal = {Soft Comput.},

volume = {24},

number = {5},

pages = {3569--3589},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ferrer2018Bio,

title = {BIN-CT: Urban waste collection based on predicting the container fill level.},

author = {Javier Ferrer and Enrique Alba},

url = {http://arxiv.org/abs/1807.01603 https://linkinghub.elsevier.com/retrieve/pii/S0303264718301333 http://dx.doi.org/10.1016/j.biosystems.2019.04.006 http://www.ncbi.nlm.nih.gov/pubmed/31004697},

doi = {10.1016/j.biosystems.2019.04.006},

issn = {1872-8324},

year  = {2019},

date = {2019-12-01},

journal = {BioSystems},

volume = {186},

pages = {103962},

abstract = {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 waste treatment. In this article, we present a free intelligent software system called BIN-CT (BIN for the CiTy), based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data. The objective of the system is to reduction the cost of the waste collection service minimizing the distance traveled by a truck to collect the waste from a container, thereby reducing the fuel consumption. At the same time the quality of service for the citizen is increased, avoiding the annoying overflows of containers thanks to the accurate fill-level predictions given by BIN-CT. In this article we show the features of our software system, illustrating its operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary trips to containers, reduces the distance traveled to collect a container by 20%, and generates routes 33.2% shorter than the routes used by the company. Therefore it enables a considerable reduction of total costs and harmful emissions thrown up into the atmosphere.},

keywords = {Forecasting, Machine learning, Recycling, Routes generation, Waste collection},

pubstate = {published},

tppubtype = {article}

}

@article{315,

title = {Efficient anytime algorithms to solve the bi-objective Next Release Problem},

author = {Miguel Ángel Dom{'i}nguez-R{'i}os and Francisco Chicano and Enrique Alba and Isabel del Águila and José del Sagrado},

url = {https://hdl.handle.net/10630/17907},

doi = {10.1016/j.jss.2019.06.097},

issn = {01641212},

year  = {2019},

date = {2019-06-01},

journal = {Journal of Systems and Software},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cintrano2019,

title = {Facing robustness as a multi-objective problem: A bi-objective shortest path problem in smart regions},

author = {Christian Cintrano and Francisco Chicano and Enrique Alba},

doi = {10.1016/J.INS.2019.07.014},

issn = {0020-0255},

year  = {2019},

date = {2019-11-01},

journal = {Information Sciences},

volume = {503},

pages = {255--273},

publisher = {Elsevier},

abstract = {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 gas emissions. Our proposal treats robustness from a multi-objective point of view. We model the parameters that define each instance as random variables, described through their mean and variance. In this way, we can obtain efficient solutions that are also less sensitive to changes in the environment. We run different types of algorithms in multiple instances to solve this problem so that we obtain a global view of the behavior of different techniques. All experimentation uses a scenario based on real data: The province of Malaga, Spain. This realistic settlement for our study allows us to test the applicability of our model in final systems for the citizens. The results clearly state the interest of our proposal for tackling robustness and represents a new state-of-the-art in smart mobility, an always appealing feature of works, that could lead to an industrial prototype.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{317,

title = {Facing robustness as a multi-objective problem: A bi-objective~shortest~path problem in smart regions},

author = {C Cintrano and F Chicano and E Alba},

url = {https://linkinghub.elsevier.com/retrieve/pii/S002002551930619X},

doi = {10.1016/j.ins.2019.07.014},

issn = {00200255},

year  = {2019},

date = {2019-07-01},

journal = {Information Sciences},

volume = {503},

pages = {255 - 273},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{morell2019jsdoop,

title = {JSDoop and TensorFlow. js: Volunteer Distributed Web Browser-Based Neural Network Training},

author = {José Á Morell and Andrés Camero and Enrique Alba},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Access},

volume = {7},

pages = {158671--158684},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ruiz_preference-based_2019,

title = {Preference-based evolutionary multi-objective optimization for portfolio selection: a new credibilistic model under investor preferences},

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

url = {https://doi.org/10.1007/s10898-019-00782-1},

doi = {10.1007/s10898-019-00782-1},

issn = {1573-2916},

year  = {2019},

date = {2019-05-01},

journal = {Journal of Global Optimization},

volume = {76},

number = {2},

pages = {295--315},

abstract = {We propose a new credibility portfolio selection model, in which a measure of loss aversion is introduced as an objective function, joint to the expected value of the returns and the below-mean absolute semi-deviation as a risk measure. The uncertainty of the future returns is directly approximated using the historical returns on the portfolios, so the uncertain return on a given portfolio is modeled as an LR-power fuzzy variable. Quantifying the uncertainty by means of a credibility distribution allows us to measure the investors' loss aversion as the credibility of achieving a non-positive return, which is better perceived by investors than other measures of risk. Furthermore, we analyze the relationships between the three objective functions, showing that the risk measure and the loss aversion function are practically uncorrelated. Thus, the information provided by these criteria do not overlap each other. In order to generate several non-dominated portfolios taking into account the investor's preferences and that the problem is non-linear and non-convex, we apply up to three preference-based EMO algorithms. These algorithms allow to approximate a part of the Pareto optimal front called region of interest. We analyze three investor profiles taking into account their loss-adverse attitudes: conservative, cautious and aggressive. A computational study is performed with data of the Spanish stock market, showing the important role played by the loss aversion function to generate a diversified set of non-dominated portfolios fitting the expectations of each investor.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ferrer2019,

title = {Reliable simulation-optimization of traffic lights in a real-world city},

author = {Javier Ferrer and Manuel López-Ibá{ñ}ez and Enrique Alba},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1568494619301346},

doi = {10.1016/j.asoc.2019.03.016},

issn = {15684946},

year  = {2019},

date = {2019-03-01},

journal = {Applied Soft Computing Journal},

volume = {78},

pages = {697--711},

abstract = {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 no single traffic scenario is a precise representation of the real system, and the fitness of any candidate solution (traffic-light program) will vary when deployed on the city. Thus, ideal TLPs should not only have an optimized fitness, but also a high reliability, i.e., low fitness variance, against the uncertainties of the real-world. Earlier traffic-light optimization methods, e.g., based on genetic algorithms, often simulate a single traffic scenario, which neglects variance in the real-world, leading to TLPs not optimized for reliability. Our main contributions in this work are the following: (a) the analysis of the importance of reliable solutions for TLP optimization, even when all traffic scenarios are consistent with the real-world data and highly correlated; (b) the adaptation of IRACE, an iterated racing algorithm that is able to dynamically adjust the number of traffic scenarios required to evaluate the fitness of TLPs and their reliability; (c) the use of a large real-world case study for which real-time control is not possible and where data was obtained from sensors at the street level; and (d) a thorough analysis of solutions generated by means of IRACE, a Genetic Algorithm, a Differential Evolution, a Particle Swarm Optimization and a Random Search. This analysis shows that simple strategies that simulate multiple traffic scenarios are able to obtain optimized solutions with improved reliability; however, the best results are obtained by IRACE, among the algorithms evaluated.},

keywords = {Metaheuristics, Optimization, Simulation, Traffic-light planning, Uncertainty},

pubstate = {published},

tppubtype = {article}

}

@article{Camero2019b,

title = {Waste generation prediction under uncertainty in smart cities through deep neuroevolution},

author = {Andrés Camero and Jamal Toutouh and Javier Ferrer and Enrique Alba},

doi = {10.17533/udea.redin.20190736},

issn = {24222844},

year  = {2019},

date = {2019-01-01},

journal = {Revista Facultad de Ingenieria},

number = {93},

pages = {128--138},

abstract = {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 this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.},

keywords = {Deep learning, Deep neuroevolution, evolutionary algorithms, Smart cities, Waste collection},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/eswa/DahiAD18,

title = {A stop-and-start adaptive cellular genetic algorithm for mobility 

 management of GSM-LTE cellular network users},

author = {Zakaria Abdelmoiz Dahi and 

 Enrique Alba and 

 Amer Draa},

url = {https://doi.org/10.1016/j.eswa.2018.02.041},

doi = {10.1016/j.eswa.2018.02.041},

year  = {2018},

date = {2018-01-01},

journal = {Expert Syst. Appl.},

volume = {106},

pages = {290--304},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saborido_app_2018,

title = {An app performance optimization advisor for mobile device app marketplaces},

author = {Rubén Saborido and Foutse Khomh and Abram Hindle and Enrique Alba},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2210537917303529},

doi = {10.1016/j.suscom.2018.05.008},

issn = {22105379},

year  = {2018},

date = {2018-09-01},

urldate = {2018-06-15},

journal = {Sustainable Computing: Informatics and Systems},

volume = {19},

pages = {29--42},

abstract = {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 to the constrained hardware of mobile devices, users and developers have to meticulously manage the resources available and they should be given access to performance information about apps. Even if this information was available, the selection of apps would still depend on user preferences and it would require a huge cognitive effort to make optimal decisions. Considering this fact we propose APOA, a recommendation system which can be implemented in any marketplace for helping users and developers to compare apps in terms of performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{301,

title = {EARMO: An Energy-Aware Refactoring Approach for Mobile Apps},

author = {Rodrigo Morales and Ruben Saborido and Foutse Khomh and Francisco Chicano and Giuliano Antoniol},

url = {http://ieeexplore.ieee.org/document/8052533/http://xplorestaging.ieee.org/ielx7/32/4359463/08052533.pdf?arnumber=8052533},

doi = {10.1109/TSE.2017.2757486},

issn = {0098-5589},

year  = {2018},

date = {2018-09-01},

journal = {IEEE Transactions on Software Engineering},

volume = {44},

pages = {1176-1206},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{morales_earmo:_2018,

title = {EARMO: An Energy-Aware Refactoring Approach for Mobile Apps},

author = {Rodrigo Morales and Rubén Saborido and Foutse Khomh and Francisco Chicano and Giuliano Antoniol},

doi = {10.1109/TSE.2017.2757486},

issn = {0098-5589},

year  = {2018},

date = {2018-01-01},

journal = {IEEE Transactions on Software Engineering},

volume = {44},

number = {12},

pages = {1176--1206},

keywords = {Androids, Anti-patterns, Batteries, Energy consumption, Energy measurement, Humanoid robots, Mobile apps, Mobile communication, Refactoring, Search-based Software Engineering, Software, Software maintenance},

pubstate = {published},

tppubtype = {article}

}

@article{299,

title = {Editorial for the special issue on metaheuristics for combinatorial optimization},

author = {Bin Hu and Francisco Chicano},

url = {http://rdcu.be/t26e},

doi = {10.1007/s10732-017-9352-y},

issn = {1381-1231},

year  = {2018},

date = {2018-07-01},

journal = {Journal of Heuristics},

volume = {24},

pages = {239-242},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{308,

title = {Efficient Refactoring Scheduling Based On Partial Order Reduction},

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

url = {https://linkinghub.elsevier.com/retrieve/pii/S0164121218301523},

doi = {10.1016/j.jss.2018.07.076},

issn = {01641212},

year  = {2018},

date = {2018-11-01},

journal = {Journal of Systems and Software},

volume = {145},

pages = {25-51},

chapter = {25},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{295,

title = {Exact search-space size for the refactoring scheduling problem},

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

url = {http://rdcu.be/GE5y},

doi = {10.1007/s10515-017-0213-6},

issn = {0928-8910},

year  = {2018},

date = {2018-10-01},

journal = {Automated Software Engineering},

volume = {25},

pages = {195-200},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saborido_getting_2018,

title = {Getting the most from map data structures in Android},

author = {Rubén Saborido and Rodrigo Morales and Foutse Khomh and Yann-Gaël Guéhéneuc and Giuliano Antoniol},

url = {https://doi.org/10.1007/s10664-018-9607-8},

doi = {10.1007/s10664-018-9607-8},

issn = {1573-7616},

year  = {2018},

date = {2018-03-01},

journal = {Empirical Software Engineering},

volume = {23},

number = {5},

pages = {2829--2864},

abstract = {A map is a data structure that is commonly used to store data as key–value pairs and retrieve data as keys, values, or key–value pairs. Although Java offers different map implementation classes, Android SDK offers other implementations supposed to be more efficient than HashMap: ArrayMap and SparseArray variants (SparseArray, LongSparseArray, SparseIntArray, SparseLongArray, and SparseBooleanArray). Yet, the performance of these implementations in terms of CPU time, memory usage, and energy consumption is lacking in the official Android documentation; although saving CPU, memory, and energy is a major concern of users wanting to increase battery life. Consequently, we study the use of map implementations by Android developers in two ways. First, we perform an observational study of 5713 Android apps in GitHub. Second, we conduct a survey to assess developers' perspective on Java and Android map implementations. Then, we perform an experimental study comparing HashMap, ArrayMap, and SparseArray variants map implementations in terms of CPU time, memory usage, and energy consumption. We conclude with guidelines for choosing among the map implementations: HashMap is preferable over ArrayMap to improve energy efficiency of apps, and SparseArray variants should be used instead of HashMap and ArrayMap when keys are primitive types.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{310,

title = {NK Hybrid Genetic Algorithm for Clustering},

author = {Renato Tinos and Liang Zhao and Francisco Chicano and Darrell Whitley},

url = {https://ieeexplore.ieee.org/document/8341487/},

doi = {10.1109/TEVC.2018.2828643},

issn = {1089-778X1089-778X},

year  = {2018},

date = {2018-01-01},

journal = {IEEE Transactions on Evolutionary Computation},

volume = {22},

pages = {748-761},

keywords = {},

pubstate = {published},

tppubtype = {article}

}