@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},

keywords = {Cellular Genetic Algorithms, Community Detection, Genetic Algorithms, Multi-objective Optimization, Social Networks Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panizo2018algoritmo,

title = {Un Algoritmo Memético, con búsqueda local basada en Label Propagation, para detectar comunidades en redes dinámicas},

author = {Ángel Panizo-LLedot and Gema Bello Orgaz and Alfonso Ortega and David Camacho Fernández},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {XVIII Conferencia de la Asociación Espa~nola para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, Espa~na},

pages = {995--1000},

organization = {Asociación Espa~nola para la Inteligencia Artificial (AEPIA)},

keywords = {Bio-inspired Metaheuristics, Dynamic Community Detection, Genetic Algorithms, Memetic Algorithms, Social Networks Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panizo2018genetic,

title = {A genetic algorithm with local search based on label propagation for detecting dynamic communities},

author = {Ángel Panizo-LLedot and Gema Bello-Orgaz and David Camacho},

doi = {10.1007/978-3-319-99626-4_28},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {International symposium on intelligent and distributed computing},

pages = {319--328},

organization = {Springer},

keywords = {Dynamic Community Detection, Genetic Algorithms, Memetic Algorithms, Social Networks Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{panizo2018community,

title = {Community finding in dynamic networks using a genetic algorithm improved via a hybrid immigrants scheme},

author = {Ángel Panizo-LLedot and Gema Bello-Orgaz and Alfonso Ortega and David Camacho},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018)},

pages = {591--598},

organization = {World Scientific},

keywords = {Dynamic Community Detection, Genetic Algorithms, Social Networks Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {https://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 = {Constraint Satisfaction Problems, Genetic Algorithms, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 = {Constraint Satisfaction Problems, Genetic Algorithms, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},

pubstate = {published},

tppubtype = {inproceedings}

}