Dr. Cristian Ramirez Atencia

@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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pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ramirez2020tea,

title = {T-EA: A traceable evolutionary algorithm},

author = {Cristian Ramirez-Atencia and Tobias Benecke and Sanaz Mostaghim},

doi = {10.1109/CEC48606.2020.9185615},

isbn = {978-1-7281-6929-3},

year  = {2020},

date = {2020-09-03},

urldate = {2020-09-03},

booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)},

pages = {1--8},

organization = {IEEE},

abstract = {In this paper, the influence of the initial population into successive generations in Evolutionary Algorithms (EAs) is studied as a problem-independent approach. For this purpose, the Traceable Evolutionary Algorithm (T-EA) is proposed. This algorithm keeps track of the influence of the individuals from the initial population over the generations of the algorithm. The algorithm has been implemented for both bit-string and integer vector representations. In addition, in order to study the general influence of each individual, new impact factor metrics have been proposed. In this way, we aim to provide tools to measure the influence of initial individuals on the final solutions. As a proof of concept, three classical optimization problems (One Max, 0/1 Knapsack and Unbounded Knapsack problems) are used. We provide a framework that allows to explain why some individuals in the initial population work better than others in relation with the corresponding fitness values.},

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pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/cec/Ramirez-Atencia17,

title = {New Artificial Intelligence approaches for future UAV Ground Control Stations},

author = {Cristian Ramírez-Atencia and Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

url = {https://doi.org/10.1109/CEC.2017.7969645},

doi = {10.1109/CEC.2017.7969645},

isbn = {978-1-5090-4601-0},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017},

pages = {2775--2782},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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},

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 = {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.},

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pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{LosadaSuarez2016,

title = {Desarrollo de una plataforma basada en Unity3D para la aplicación de IA en videojuegos},

author = {Óscar Manuel Losada Suárez and Víctor Rodríguez-Fernández and Cristian Ramírez-Atencia and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2017/03/Desarrollo-de-una-plataforma-basada-en-Unity3D-para-la-aplicación-de-IA-en-videojuegos.pdf},

issn = {16130073},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {3rd Congreso de la Sociedad Española para las Ciencias del Videojuego (CoSECiVi 2016)},

volume = {1682},

pages = {135--146},

publisher = {CEUR Workshop},

address = {Barcelona, Spain},

abstract = {La utilización intensiva de diferentes técnicas relacionadas con la Inteligencia Artificial (IA) en el área de los videojuegos ha demostrado ser una necesidad para el campo. El uso de estas técnicas permite dotar de una mayor flexibilidad y adaptabilidad a los juegos que es muy apreciada por los jugadores. Temas como la generación procedimental de contenido, la creación de agentes que puedan jugar a un videojuego de forma competente, o de agentes cuya conducta sea indistinguible de la de un jugador humano atraen a una cantidad creciente de investigadores. El objetivo de este trabajo es la presentación de una plataforma basada en el motor Unity3D que permita de manera simple la integración y prueba de algoritmos de IA. La plataforma ofrecerá como nuevas características, adicionales a las ya disponibles en la actualidad, la utilización de un entorno 3D, el desarrollo de un juego innovador (basado en múltiples agentes), y la exploración de aspectos de juego como el análisis del terreno, la cooperación entre agentes independientes y heterogéneos, la comunicación de información entre los mismos y la formación de jerarquías.},

keywords = {},

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.},

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pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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.},

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pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/iccci/Ramirez-Atencia15,

title = {Performance Evaluation of Multi-UAV Cooperative Mission Planning Models},

author = {Cristian Ramírez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},

url = {http://dx.doi.org/10.1007/978-3-319-24306-1_20 

http://aida.etsisi.upm.es/wp-content/uploads/2015/09/ramirez-atenciaPerformance.pdf},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II},

pages = {203--212},

crossref = {DBLP:conf/iccci/2015-2},

abstract = {The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens.},

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pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rodriguez2015summary,

title = {A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game},

author = {Victor Rodriguez-Fernández and Cristian Ramirez-Atencia and David Camacho},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {2nd Congreso de la Sociedad Espa~nola para las Ciencias del Videojuego (CoSeCiVi 2015)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ramirez2014solving,

title = {Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms},

author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},

year  = {2014},

date = {2014-09-12},

urldate = {2014-09-12},

booktitle = {Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014)},

abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. We study how this problem can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). This problem contains several constraints assuring UAVs are assigned to tasks they have enough characteristics to perform, and soft-constraints for optimizing the time and fuel spent in the process. Our goal is to implement this model and then try to solve it using Genetic Algorithms (GAs). For this purpose, we will carry out a mission simulation containing m UAVs with different sensors and characteristics located in different waypoints, and n requested tasks varying mission priorities. The GA will match the model constraints and use a multi-objective function in order to minimize the cost.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ramirez2014simple,

title = {A simple CSP-based model for unmanned air vehicle mission planning},

author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

booktitle = {2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings},

pages = {146--153},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ramirez2014branching,

title = {Branching to find feasible solutions in unmanned air vehicle mission planning},

author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},

pages = {286--294},

organization = {Springer, Cham},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}