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Javadi, Mahrokh; Ramirez-Atencia, Cristian; Mostaghim, Sanaz
Combining manhattan and crowding distances in decision space for multimodal multi-objective optimization problems Book Section
In: Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, vol. 55, pp. 131–145, Springer, Cham, 2021, ISBN: 978-3-030-57422-2.
@incollection{javadi2021combining,
title = {Combining manhattan and crowding distances in decision space for multimodal multi-objective optimization problems},
author = {Mahrokh Javadi and Cristian Ramirez-Atencia and Sanaz Mostaghim},
doi = {10.1007/978-3-030-57422-2_9},
isbn = {978-3-030-57422-2},
year = {2021},
date = {2021-01-01},
booktitle = {Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences},
volume = {55},
pages = {131--145},
publisher = {Springer, Cham},
series = {Computational Methods in Applied Sciences},
abstract = {This paper presents a new variant of the Non-dominated Sorting Genetic Algorithm to solve Multimodal Multi-objective optimization problems. We introduce a novel method to augment the diversity of solutions in decision space by combining the Manhattan and crowding distance. In our experiments, we use six test problems with different levels of complexity to examine the performance of our proposed algorithm. The results are compared with NSGA-II and NSGA-II-WSCD algorithms. Using IGDX and IGD performance indicators, we demonstrate the superiority of our proposed method over the rest of competitors to provide a better approximation of the Pareto Set (PS) while not getting much worse results in objective space.},
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Javadi, Mahrokh; Ramirez-Atencia, Cristian; Mostaghim, Sanaz
A novel grid-based crowding distance for multimodal multi-objective optimization Proceedings Article
In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8, IEEE 2020, ISBN: 978-1-7281-6929-3.
@inproceedings{javadi2020novel,
title = {A novel grid-based crowding distance for multimodal multi-objective optimization},
author = {Mahrokh Javadi and Cristian Ramirez-Atencia and Sanaz Mostaghim},
doi = {10.1109/CEC48606.2020.9185835},
isbn = {978-1-7281-6929-3},
year = {2020},
date = {2020-09-03},
booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)},
pages = {1--8},
organization = {IEEE},
abstract = {Preserving diversity in decision space plays an important role in Multimodal Multi-objective Optimization problems (MMOPs). Due to the lack of mechanisms to keep different solutions with the same fitness value, most of the available Multi-objective Evolutionary Algorithms (MOEAs) perform poorly when applied to MMOPs. To deal with these problems, this paper proposes a novel method for diversity preserving in the decision space. To this end, the concept of grid-based crowding distance for decision space is introduced. Furthermore, to keep a good diversity of solutions in both decision and objective spaces, we propose different frameworks by combining this method with crowding distance in decision space, crowding distance in objective space, and the weighted sum of both crowding distances. In order to evaluate the performance of these frameworks, we integrate them into the diversity preserving part of the NSGA-II algorithm, and compare them with the NSGA-II (as the baseline algorithm) and the state-of-the-art multimodal multi-objective optimization algorithms on ten different MMOPs with different levels of complexity.},
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Panizo-LLedot, Angel; Bello-Orgaz, Gema; Camacho, David
A multi-objective genetic algorithm for detecting dynamic communities using a local search driven immigrant’s scheme Journal Article
In: Future Generation Computer Systems, vol. 110, pp. 960–975, 2020.
@article{panizo2020multi,
title = {A multi-objective genetic algorithm for detecting dynamic communities using a local search driven immigrant’s scheme},
author = {Angel Panizo-LLedot and Gema Bello-Orgaz and David Camacho},
doi = {10.1016/j.future.2019.10.041},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Future Generation Computer Systems},
volume = {110},
pages = {960--975},
publisher = {Elsevier},
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Pedemonte, Martín; Panizo-LLedot, Ángel; Bello-Orgaz, Gema; Camacho, David
Exploring multi-objective cellular genetic algorithms in community detection problems Proceedings Article
In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 223–235, Springer 2020.
@inproceedings{pedemonte2020exploring,
title = {Exploring multi-objective cellular genetic algorithms in community detection problems},
author = {Martín Pedemonte and Ángel Panizo-LLedot and Gema Bello-Orgaz and David Camacho},
doi = {10.1007/978-3-030-62365-4_22},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},
pages = {223--235},
organization = {Springer},
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Ramirez-Atencia, Cristian; Camacho, David
Constrained multi-objective optimization for multi-UAV planning Journal Article
In: Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 6, pp. 2467–2484, 2019, ISSN: 1868-5145.
@article{ramirez2019constrained,
title = {Constrained multi-objective optimization for multi-UAV planning},
author = {Cristian Ramirez-Atencia and David Camacho},
doi = {10.1007/s12652-018-0930-0},
issn = {1868-5145},
year = {2019},
date = {2019-06-01},
journal = {Journal of Ambient Intelligence and Humanized Computing},
volume = {10},
number = {6},
pages = {2467--2484},
publisher = {Springer Berlin Heidelberg},
abstract = {Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.},
keywords = {},
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tppubtype = {article}
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Ramirez-Atencia, Cristian; Ser, Javier Del; Camacho, David
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning Journal Article
In: Swarm and Evolutionary Computation, vol. 44, pp. 480–495, 2019, ISSN: 2210-6502.
@article{ramirez2019weighted,
title = {Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning},
author = {Cristian Ramirez-Atencia and Javier Del Ser and David Camacho},
doi = {10.1016/j.swevo.2018.06.005},
issn = {2210-6502},
year = {2019},
date = {2019-02-01},
journal = {Swarm and Evolutionary Computation},
volume = {44},
pages = {480--495},
publisher = {Elsevier},
abstract = {Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach.},
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Jiménez-Fernández, Silvia; Camacho-Gómez, C.; Mallol-Poyato, Ricardo; Fernández, Juan Carlos; Ser, Javier Del; Portilla-Figueras, Antonio; Salcedo-Sanz, Sancho
Optimal microgrid topology design and siting of distributed generation sources using a multi-objective substrate layer Coral Reefs Optimization algorithm Journal Article
In: Sustainability, vol. 11, no. 1, 2019.
@article{nokey,
title = {Optimal microgrid topology design and siting of distributed generation sources using a multi-objective substrate layer Coral Reefs Optimization algorithm},
author = {Silvia Jiménez-Fernández and C. Camacho-Gómez and Ricardo Mallol-Poyato and Juan Carlos Fernández and Javier Del Ser and Antonio Portilla-Figueras and Sancho Salcedo-Sanz},
doi = {10.3390/su11010169},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Sustainability},
volume = {11},
number = {1},
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Ramirez-Atencia, Cristian
Automated mission planning and decision support systems for multiple unmanned aerial vehicles PhD Thesis
Universidad Autónoma de Madrid, 2018.
@phdthesis{ramirez2018automated,
title = {Automated mission planning and decision support systems for multiple unmanned aerial vehicles},
author = {Cristian Ramirez-Atencia},
url = {http://hdl.handle.net/10486/686590},
year = {2018},
date = {2018-10-22},
school = {Universidad Autónoma de Madrid},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
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Ramirez-Atencia, Cristian; R-Moreno, Maria D; Camacho, David
Handling swarm of UAVs based on evolutionary multi-objective optimization Journal Article
In: Progress in Artificial Intelligence, vol. 6, iss. 3, pp. 263-274, 2017, ISSN: 2192-6352.
@article{Ramirez-Atencia2017,
title = {Handling swarm of UAVs based on evolutionary multi-objective optimization},
author = {Cristian Ramirez-Atencia and Maria D R-Moreno and David Camacho},
url = {http://link.springer.com/10.1007/s13748-017-0123-7},
doi = {10.1007/s13748-017-0123-7},
issn = {2192-6352},
year = {2017},
date = {2017-09-01},
urldate = {2017-01-01},
journal = {Progress in Artificial Intelligence},
volume = {6},
issue = {3},
pages = {263-274},
publisher = {Springer Berlin Heidelberg},
abstract = {The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.},
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Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms Journal Article
In: Soft Computing, vol. 21, iss. 17, pp. 4883-4900, 2017, ISSN: 1432-7643; 1433-7479.
@article{Ramirez-Atencia2016c,
title = {Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
doi = {10.1007/s00500-016-2376-7},
issn = {1432-7643; 1433-7479},
year = {2017},
date = {2017-09-01},
urldate = {2016-01-01},
journal = {Soft Computing},
volume = {21},
issue = {17},
pages = {4883-4900},
publisher = {Springer Berlin Heidelberg},
abstract = {Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find.},
keywords = {},
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Ramirez-Atencia, Cristian; Mostaghim, Sanaz; Camacho, David
A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems Proceedings Article
In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1216–1223, ACM, Berlin, Germany, 2017, ISBN: 978-1-4503-4920-8.
@inproceedings{Ramirez-Atencia2017b,
title = {A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems},
author = {Cristian Ramirez-Atencia and Sanaz Mostaghim and David Camacho},
url = {http://doi.acm.org/10.1145/3071178.3071319},
doi = {10.1145/3071178.3071319},
isbn = {978-1-4503-4920-8},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {1216--1223},
publisher = {ACM},
address = {Berlin, Germany},
series = {GECCO '17},
abstract = {The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.},
keywords = {},
pubstate = {published},
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}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems Proceedings Article
In: XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), pp. 305–314, 2016.
@inproceedings{Ramirez-Atencia2016a,
title = {A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2017/03/A-Weighted-Penalty-Fitness-for-a-Hybrid-MOGA-CSP-to-solve-Mission-Planning-Problems.pdf},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016)},
pages = {305--314},
abstract = {Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results.},
keywords = {},
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}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning Proceedings Article
In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, ISBN: 978-1-5090-4240-1, 978-1-5090-4241-8.
@inproceedings{Ramirez-Atencia2016b,
title = {MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
doi = {10.1109/SSCI.2016.7850235},
isbn = {978-1-5090-4240-1, 978-1-5090-4241-8},
year = {2016},
date = {2016-01-01},
booktitle = {2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
abstract = {From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered.},
keywords = {},
pubstate = {published},
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}
Martín, Alejandro; Menéndez, Héctor D; Camacho, David
MOCDroid: multi-objective evolutionary classifier for Android malware detection Journal Article
In: Soft Computing, pp. 1–11, 2016.
@article{martin2016mocdroid,
title = {MOCDroid: multi-objective evolutionary classifier for Android malware detection},
author = {Alejandro Martín and Héctor D Menéndez and David Camacho},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Soft Computing},
pages = {1--11},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, María D; Camacho, David
A Hybrid MOGA-CSP for Multi-UAV Mission Planning Proceedings Article
In: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1205–1208, ACM 2015.
@inproceedings{ramirez2015hybrid,
title = {A Hybrid MOGA-CSP for Multi-UAV Mission Planning},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and María D R-Moreno and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/ramirez-atenciaHybrid.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference},
pages = {1205--1208},
organization = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rodriguez-Fernandez, Victor; Ramirez-Atencia, Cristian; Camacho, David
A multi-UAV Mission Planning videogame-based framework for player analysis Proceedings Article
In: Evolutionary Computation (CEC), 2015 IEEE Congress on, pp. 1490–1497, IEEE 2015.
@inproceedings{rodriguez2015multi,
title = {A multi-UAV Mission Planning videogame-based framework for player analysis},
author = {Victor Rodriguez-Fernandez and Cristian Ramirez-Atencia and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/07257064.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Evolutionary Computation (CEC), 2015 IEEE Congress on},
pages = {1490--1497},
organization = {IEEE},
abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.},
keywords = {},
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tppubtype = {inproceedings}
}
Menendez, Hector D; Barrero, David F; Camacho, David
A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm Proceedings Article
In: Evolutionary Computation (CEC), 2014 IEEE Congress on, pp. 2724–2731, IEEE 2014.
@inproceedings{menendez2014co,
title = {A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm},
author = {Hector D Menendez and David F Barrero and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Evolutionary Computation (CEC), 2014 IEEE Congress on},
pages = {2724--2731},
organization = {IEEE},
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Menendez, Hector D; Camacho, David
A Multi-Objective Graph-based Genetic Algorithm for Image Segmentation Proceedings Article
In: Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on, pp. 234–241, IEEE 2014.
@inproceedings{menendez2014multi,
title = {A Multi-Objective Graph-based Genetic Algorithm for Image Segmentation},
author = {Hector D Menendez and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on},
pages = {234--241},
organization = {IEEE},
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Barrero, David F; Menéndez, Héctor D; Camacho, David
A Multi-Objective Genetic Graph-based Clustering Algorithm with Memory Optimization Conference
IEEE Congress on Evolutionary Computation (CEC 2013), 2013.
@conference{Hector-2013-CEC,
title = {A Multi-Objective Genetic Graph-based Clustering Algorithm with Memory Optimization},
author = {David F Barrero and Héctor D Menéndez and David Camacho},
url = {https://atc1.aut.uah.es/~david/cec2013/Menendez2013CEC.pdf},
year = {2013},
date = {2013-06-20},
urldate = {2013-06-20},
booktitle = {IEEE Congress on Evolutionary Computation (CEC 2013)},
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}