INF - Instituto de Informática

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Detecção online de dispositivos sem fio intrusos usando o sinal eletromagnético de transmissão
(Universidade Federal de Goiás, 2022-09-23) Abreu, Marcos Felipe Barboza de; Vieira, Flávio Henrique Teles; http://lattes.cnpq.br/0920629723928382; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Klautau Júnior, Aldebaro Barreto da Rocha; Corrêa, Sand Luz; Cardoso, Kleber Vieira; Vieira, Flávio Henrique Teles
The identification of Internet of Things (IoT) devices through the electromagnetic signal is a topic widely investigated in the literature, and this technique is considered highly accurate by several works. The use of offline techniques, that is, when there is no presence of new devices, is widely explored, but so far, systems are not found effectively using the detection of unknown devices in the online way, i.e. , one of the greatest potentials of this type of technique has not been investigated. This work presents an online system that differentiates authentic devices from intrusive devices. For this, the use of the probability matrix of classifiers is explored, aiming to identify unknown devices by them. In addition to the technique, it is also presented a system that features a modular, extensible and generic architecture, which aims to minimally interfere with the normal flow of an Internet of Things application. The system is implemented using the GNU Radio tool and experiments are presented, which aim to show the feasibility of the technique. The entire discussion is based on data collected from real environments, using devices from wireless communication technologies LoRa and ZigBee. In addition, the work analyzed data from WiFi technology, from collections found in the literature. Tests show that it is possible to identify unknown devices in the order of milliseconds, with a low error rate.
Airetama Um Arcabouço Baseado em Sistemas Multiagentes para a Implantação de Comunidades Virtuais de Prática na Web
(Universidade Federal de Goiás, 2010-10-04) ALARCÓN, Jair Abú Bechir Láscar; CARVALHO, Cedric Luiz de; http://lattes.cnpq.br/4090131106212286
The objective of this dissertation is to present the framework Airetama. This framework is based on Multiagent Systems and Semantic Web principles. It provides a semantic, distributed and open-source infrastructure for the creation of Virtual Communities of Practice on the Web. It makes possible, through the use of agents, coupling of resources and tools that use semantic technologies. Integration of semantic in the current Web has as main objective to allow such software agents can use their pages more intelligently, thus offering better service.
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based
(Universidade Federal de Goiás, 2014-09-02) Aleixo, Everton Lima; Rosa, Thierson Couto; http://lattes.cnpq.br/4414718560764818; Rosa, Thierson Couto; Camilo Júnior, Celso Gonçalves; Pereira, Denilson Alves
Memory-based algorithms are the most popular among the collaborative filtering algorithms. They use as input a table containing ratings given by users to items, known as the rating matrix. They predict the rating given by user a to an item i by computing similarities of the ratings among users or similarities of the ratings among items. In the first case Memory-Based algorithms are classified as User-based algorithms and in the second one they are labeled as Item-based algorithms. The prediction is computed using the ratings of k most similar users (or items), also know as neighbors. Memory-based algorithms are simple to understand and to program, usually provide accurate recommendation and are less sensible to data change. However, to obtain the most similar neighbors for a prediction they have to process all the data which is a serious scalability problem. Also they are sensitive to the sparsity of the input. In this work we propose an efficient and effective Item-Based that aims at diminishing the sensibility of the Memory-Based approach to both problems stated above. The algorithm is faster (almost 50%) than the traditional Item-Based algorithm while maintaining the same level of accuracy. However, in environments that have much data to predict and few to train the algorithm, the accuracy of the proposed algorithm surpass significantly that of the traditional Item-based algorithms. Our approach can also be easily adapted to be used as User-based algorithms.
Predição de estrutura terciária de proteínas com técnicas multiobjetivo no algoritmo de monte carlo
(Universidade Federal de Goiás, 2016-06-17) Almeida, Alexandre Barbosa de; Faccioli, Rodrigo Antonio; http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4710519J5; Soares, Telma Woerle de Lima; http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4717638T6; Soares, Telma Woerle de Lima; Facciolo, Rodrigo Antonio; Martins, Wellignton Santos; Leão, Salviano de Araújo
Proteins are vital for the biological functions of all living beings on Earth. However, they only have an active biological function in their native structure, which is a state of minimum energy. Therefore, protein functionality depends almost exclusively on the size and shape of its native conformation. However, less than 1% of all known proteins in the world has its structure solved. In this way, various methods for determining protein structures have been proposed, either in vitro or in silico experiments. This work proposes a new in silico method called Monte Carlo with Dominance, which addresses the problem of protein structure prediction from the point of view of ab initio and multi-objective optimization, considering both protein energetic and structural aspects. The software GROMACS was used for the ab initio treatment to perform Molecular Dynamics simulations, while the framework ProtPred-GROMACS (2PG) was used for the multi-objective optimization problem, employing genetic algorithms techniques as heuristic solutions. Monte Carlo with Dominance, in this sense, is like a variant of the traditional Monte Carlo Metropolis method. The aim is to check if protein tertiary structure prediction is improved when structural aspects are taken into account. The energy criterion of Metropolis and energy and structural criteria of Dominance were compared using RMSD calculation between the predicted and native structures. It was found that Monte Carlo with Dominance obtained better solutions for two of three proteins analyzed, reaching a difference about 53% in relation to the prediction by Metropolis.
Um Componente para Geração e Evolução de Esquemas de Bancos de Dados como Suporte à Construção de Sistemas de Informação
(Universidade Federal de Goiás, 2010-11-22) ALMEIDA, Alexandre Cláudio de; OLIVEIRA, Juliano Lopes de; http://lattes.cnpq.br/8890030829542444
An Information System (IS) has three main aspects: a database that contains data which is processed to generate business information; an application functions which transforms data in information; and business rules which control and restrict data manipulated by the functions. An IS evolves continuously to follow the corporation changes, and the database should be change to attend the new requirements. This dissertation presents a model driven approach to generate and evolve IS databases. A software component, called Especialista em Banco de Dados (EBD), was developed. There are two mapping sets for database generation: from Modelo de Meta Objeto (MMO) (used to representing IS) to Relational Model (RM), and from this to DBMS PostgreSQL SQL dialect. The component EBD is a part of a framework for modeling, building and maintaining enterprise information systems software. This component provides services to other framework components. To validate the proposed approach, Software Engineers had developed IS using the component EBD. The Dissertation main contributions are an approach to support IS database life cycle, a software architecture to generate and evolve IS database schema, an IS data representation model (MMO), a mapping specification to generate schema and stored procedures and the definition of automated operation sets to evolve IS database schema.
Métodos de visão computacional aplicados a extração de características de ambientes urbanos em imagens de satélite de baixa resolução
(Universidade Federal de Goiás, 2018-10-03) Almeida, Dyego de Oliveira; Oliveira, Leandro Luis Galdino de; http://lattes.cnpq.br/5899392002875573; Spoto, Edmundo Sérgio; Sene Junior, Iwens Gervasio
The urban growth of the population and the deforestation of greenareas are one of the most critical problems currently in Brazil. Due to mobilization of rural people to the urban, high solar irradiation and the deforestation, the Government is creating sustainable actions sustainable in order to enlarge the green areas and permeable. In this perspective, to promote this mapping effectively in large areas necessary to the use of technologies of recognition of facial features. Low-resolution satellite imagery have low cost and great coverage area coverage, and therefore apply them in identifying features is advantageous over other types of images. However, to accomplish this identification is computationally complex due to the different features present in images of this type. This work proposes an effective method of digital processing of low resolution images in the identification of features, in particular the pertinent green aáreas with average accuracy of 80.5% and detection of buildings with an average accuracy of 63%.
Alocação de recursos e posicionamento de funções virtualizadas em redes de acesso por rádio desagregadas
(Universidade Federal de Goiás, 2023-08-30) Almeida, Gabriel Matheus Faria de; Pinto, Leizer de Lima; http://lattes.cnpq.br/0611031507120144; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Pinto, Leizer de Lima; Klautau Júnior, Aldebaro Barreto da Rocha; Silva, Luiz Antonio Pereira da
Jointly choosing a functional split of the protocol stack and placement of network functions in a virtualized RAN is critical to efficiently using the access network resources. This problem represents a current research topic in 5G and Post-5G networks, which involves the challenge of simultaneously choosing the placement of virtualized functions, the routes for traffic and the management of available computing resources. In this work, we present three approaches to solve this problem considering the planning scenario and two approaches considering the network operation scenario. The first result is a Mixed Integer Linear Programming (MILP) model, considering a generic set of processing nodes and multipath routing. The second approach uses artificial intelligence and machine learning concepts, in which we formulate a deep reinforcement learning agent. The third approach used is based on search meta-heuristics, through a genetic algorithm. The last two approaches are Markov Decision Process (MDP) formulations that consider dynamic demand on radio units. In all formulations, the objective is to maximize the network function’s centralization while minimizing positioning cost. Analysis of the solutions and comparison of their results show that exact approaches such as MILP naturally provide the best solution. However, in terms of efficiency, the genetic algorithm has the best search time, finding a high quality solution in a few seconds. The deep reinforcement learning agent presents a high convergence, finding high quality solutions for the problem and showing problem generalization capacity with different topologies. Finally, the formulations considering the network operation scenario with dynamic demand are highly complex due to the size of the action space
Uso de Seleção de Características da Wikipedia na Classificação Automática de Textos.
(Universidade Federal de Goiás, 2012-09-20) Alvarenga, Leonel Diógenes Carvalhaes; Rosa, Thierson Couto; http://lattes.cnpq.br/4414718560764818
The traditional methods of text classification typically represent documents only as a set of words, also known as "Bag of Words"(BOW). Several studies have shown good results on making use of thesauri and encyclopedias as external information sources, aiming to expand the BOW representation by the identification of synonymy and hyponymy relationships between present terms in a document collection. However, the expansion process may introduce terms that lead to an erroneous classification. In this paper, we propose the use of feature selection measures in order to select features extracted from Wikipedia in order to improve the efectiveness of the expansion process. The study also proposes a feature selection measure called Tendency Factor to One Category (TF1C), so that the experiments showed that this measure proves to be competitive with the other measures Information Gain, Gain Ratio and Chisquared, in the process, delivering the best gains in microF1 and macroF1, in most experiments. The full use of features selected in this process showed to be more stable in assisting the classification, while it showed lower performance on restricting its insertion only to documents of the classes in which these features are well punctuated by the selection measures. When applied in the Reuters-21578, Ohsumed first - 20000 and 20Newsgroups collections, our approach to feature selection allowed the reduction of noise insertion inherent in the expansion process, and improved the results of use hyponyms, and demonstrated that the synonym relationship from Wikipedia can also be used in the document expansion, increasing the efectiveness of the automatic text classification.
Projeto InVision Framework: Um framework para suportar a criação e uso de jogos no ensino
(Universidade Federal de Goiás, 2011-05-31) ALVES, Daniel Ferreira Monteiro; ALBUQUERQUE, Eduardo Simões de; http://lattes.cnpq.br/8181318469884254
The number of people joining the Computer Science course, in the last years, is decreasing. Among those who enter, just a few are able to graduate, because there is a great retention rate and dropout, particularly among the introductory courses in algorithms and programming. The use of games as a motivational factor is a subject much studied in recent years, achieving good results for this problem. However, for the implementation of games in education, using a constructionist approach, students are required to build games. Several tools are available for this job, but there is a big difference in usability between the educational tools (that focus on educational programming) and those specific for creating games. This work proposes a framework for building games, being supported by an extensible application through scripts which allow it to be adapted for use in various disciplines throughout the course, and not only in introductory courses.
Predição do tempo de durações de processos e de movimentações processuais na esfera trabalhista
(Universidade Federal de Goiás, 2019-01-12) Amaral, Ayrton Denner da Silva; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Silva, Nádia Félix Felipe da; Marques, Thyago Carvalho
The prediction of legal proceeding movements is a relevant problem in the juridical context. Predictability is an important variable in sizing the work in attorneys offices. This works proposes an artificial neural network architecture to predict proceedings movements in Brazilian labor court. Despite the recent advances in the use of machine learning techniques and natural language processing, the problem in juridical context has its own characteristics by geographic and linguistic contexts. As a case study, a proceedings database of the year 2015 and from the same district from the labor sphere was used, due to the volume of data available.
Agente para suporte à decisão multicritério em gestão pública participativa
(Universidade Federal de Goiás, 2014-09-26) Amorim, Leonardo Afonso; Patto, Vinicius Sebba; Bulcão Neto, Renato de Freitas; http://lattes.cnpq.br/5627556088346425; Bulcão Neto, Renato de Freitas; Sene Junior, Iwens Gervásio; Patto, Vinicius Sebba; Cruz Junior, Gelson da
Decision making in public management is associated with a high degree of complexity due to insufficient financial resources to meet all the demands emanating from various sectors of society. Often, economic activities are in conflict with social or environmental causes. Another important aspect in decision making in public management is the inclusion of various stakeholders, eg public management experts, small business owners, shopkeepers, teachers, representatives of social and professional classes, citizens etc. The goal of this master thesis is to present two computational agents to aid decision making in public management as part of ADGEPA project: Miner Agent (MA) and Agent Decision Support (DSA). The MA uses data mining techniques and DSA uses multi-criteria analysis to point out relevant issues. The context in which this work fits is ADGEPA project. The ADGEPA (which means Digital Assistant for Participatory Public Management) is an innovative practice to support participatory decision making in public resources management. The main contribution of this master thesis is the ability to assist in the discovery of patterns and correlations between environmental aspects that are not too obvious and can vary from community to community. This contribution would help the public manager to make systemic decisions that in addition to attacking the main problem of a given region would decrease or solve other problems. The validation of the results depends on actual data and analysis of public managers. In this work, the data were simulated.
Implementação de princípios de gamificação adaptativa em uma aplicação mHealth
(Universidade Federal de Goiás, 2023-08-25) Anjos, Filipe Maciel de Souza dos; Carvalho, Sergio Teixeira de; http://lattes.cnpq.br/2721053239592051; Carvalho, Sergio Teixeira de; Mata, Luciana Regina Ferreira da; Berretta, Luciana de Oliveira
This work describes the implementation of a gamified mHealth application called IUProst for the treatment of urinary incontinence through the performance of pelvic exercises for men who have undergone prostate removal surgery. The development of the application followed the guidelines of Framework L, designed to guide the creation of gamified mHealth applications. The initial version of IUProst was exclusively focused on the self-care dimension of Framework L and was released in November 2022. It was used by hundreds of users seeking the treatment provided by the application. Subsequently, the Gamification dimension of Framework L was employed to gamify IUProst. During the process of implementing game elements, it was noted that there were no clear definitions of how to implement the components to allow for gamification adaptation based on user profiles. To address this gap, an implementation model for gamification components was developed to guide developers in creating gamification that could adapt to the user profile dynamics proposed by the adaptive gamification of Framework L. Therefore, the contributions of this research include delivering a gamified mHealth application, analyzing usage data generated by the gamified application, and providing an implementation model for game components that were incorporated into Framework L, enabling the use of components in the context of adaptive gamification. The gamified version of IUProst was published in July 2023 and was used for 30 days until the writing of this dissertation. The results obtained demonstrate that during the gamified month, patients performed approximately 2/3 more exercises compared to the previous two months, reaching 61% of the total exercises performed during the three months analyzed. The data confirmed the hypothesis that game components indeed contribute to patient engagement with the application and also highlighted areas for improvement in the mHealth application.
Avaliação do comportamento dos pontos fiduciais faciais durante o envelhecimento humano
(Universidade Federal de Goiás, 2017-03-04) Aquino, Cleiton Paiva; Oliveira, Leandro Luís Galdino de; http://lattes.cnpq.br/5899392002875573; Oliveira, Leandro Luís Galdino de; Seoliato, Araceli Aparecida; Albuquerque, Eduardo Simões de
Facial aging is known as a complex process that varies the shape and texture of thefacial area. Variations in shape include variation in craniofacial structure, while texture variation includes skin coloration, appearance of facial lines and wrinkles. Shape and texture are both considered the most common forms of facial aging patterns. Fiducial points are control points on an object that define characteristic regions with properties of interest to the application. Thus, the objective of this work is to evaluate the behavior offacial fiducial points during human aging. An evaluation was performed in a statistical manner and through classification, through the characteristics vectors obtained throughfacial fiducial points. For the accomplishment of this work, we had a social motivation,that is the aid in the search for missing persons, and another one of technical character,that is the perfection of facial recognition systems. Among the results we can highlight the behavior of increase, reduction and stabilization among some fiducial points. With regard to classification, we obtained a result of 84.29 % of correct answers when we compared the class with people under 20 years old and the class with people between 20 and 39 years old from the black men’s groups
Uma estratégia para a avaliação e evolução de teste funcional de software
(Universidade Federal de Goiás, 2012) Arantes, Gilmar Ferreira; Leitão Júnior, Plínio de Sá; http://lattes.cnpq.br/4480334653242457
Software Testing is part of software quality assurance activities. It aims to uncover the presence of defects, that can be inserted in various stages of software develop- ment. Several techniques are used in the testing activity, highlighting the functional ones, which derive test requirements from the software specification. The research faces the problem of how to evolve the functional testing strategies with low costs, relative to the amount of test cases needed, without compromising the number of uncovered defects. A systematic review was planned and executed, based on formu- lated questions so as to answer the research problem. Such review supported the definition of a new criterion for functional testing, the Systematic Functional Test with Decision Table Application (TFS-DT), which is an extension of Systematic Software Testing (TFS) and provides joint application of criteria: Partitioning Equi- valence Classes, Boundary Value Analysis and Decision Table. The TFS-DT defines a strategy based on a set of requirements and has a process in order to apply the strategy in a systematic manner. Three empirical studies were applied with promi- sing results compared to TFS: all of them reduces at least half the adequated set without impact on the number of uncovered defects.
Seleção e geração de características utilizando regras de associação para o problema de ordenação de resultados de máquinas de buscas
(Universidade Federal de Goiás, 2014-08-29) Araujo, Carina Calixto Ribeiro de; Rosa, Thierson Couto; http://lattes.cnpq.br/4414718560764818; Rosa, Thierson Couto; Gonçalves, Marcos André; Longo, Humberto José
Information Retrieval is an area of IT that deals with document storage and the information retrieval in these documents. With the advent of the Internet, the number of documents produced has increased as well as the need to retrieve the information more accurately. Many approaches have been proposed to meet these requirements and one of them is Learning to rank (L2R). Despite major advances achieved in the accuracy of retrived documents, there is still considerable room for improvement. This master thesis proposes the use of feature selection and generation using association rules to improve the accuracy of the L2R methods.
Uma investigação da correspondência entre mutações e avisos relatados por ferramenta de análise estática
(Universidade Federal de Goiás, 2015-12-04) Araújo, Claudio Antônio de; Vincenzi, Auri Marcelo Rizzo; http://lattes.cnpq.br/0611351138131709; Vincenzi, Auri Marcelo Rizzo; Valente, Marco Túlio de Oliveira; Lucena, Fábio Nogueira de
Traditionally, mutation testing is used for test set and/or test criteria evaluation once it is considered a good fault model. Since static analyzers, in general, report a substantial number of false positive warnings, Objective: This paper uses mutation testing for evaluating an automated static analyzer. The intention of this study is to define a prioritization approach of static warnings based on their correspondence with mutations. Method: We used mutation operators as a fault model to evaluate the direct correspondence between mutations and static warnings. The main advantage of using mutation operators is that they generate a large number of programs containing faults of different types, which can be used to decide the ones most probable to be detected by static analyzers. Results: The results obtained for a set of open-source programs indicate that: 1) correspondence exists when considering specific mutation operators such that static warnings may be prioritized based on their correspondence level with mutations; 2) correspondence exists when considering specific warning categories such that, assuming we perform static analysis considering these warning categories, mutation operators may be prioritized based on their correspondence level with warnings. Conclusion: It is possible to provide an incremental testing strategy aiming at reducing the cost of both static analysis and mutation testing using the correspondence information. On the other hand, knowing that Mutation Test has a high application cost, we identified mutations of some specific mutation operators, which an automatic static analyzer is not able to detect. Therefore, this information can used to prioritize the order of applying mutation operators incrementally considering, firstly, those with no correspondence with static warnings.
Análise dos microdados do Enade: proposta de uma ferramenta de exploração utilizando mineração de dados
(Universidade Federal de Goiás, 2019-12-20) Araújo, Rodrigo Alexandrino; Brancher, Jacques Duílio; http://lattes.cnpq.br/7909976127880843; Brancher, Jacques Duílio; Sanches, Danilo Sipoli; Camos, Vitor Valério Souza
One way to analyze higher education institutions and student performance is through the National Student Performance Examination (ENADE). From its results it is possible to make intelligent decisions for the improved teaching-learning process. However, in the analysis reports provided by Anísio Teixeira National Institute for Educational Studies and Research (INEP) only descriptive analyzes are available. Although the Institute provides ENADE’s evidence-related micro-data, advanced knowledge in data analysis and statistics is required to obtain more in-depth information about candidates. That said, this paper aims to use KDD techniques to develop an exploratory analysis tool for Enade microdata, together with a classification model capable of predicting student performance. For the elaboration of rules, several decision tree classification algorithms were used, in which CART stood out. The end result was a data analysis tool, which allows comparing higher education courses and institutions, and providing the best view of this information for the purpose of assisting decision making. Finally, an online questionnaire was distributed so that teachers, students and coordinators could evaluate and validate the developed system. After this study, the tool proved to be satisfactory and fulfills what is promised, and serves as a motivation to improve the work developed.
Um modelo ontológico e um serviço de gerenciamento de dados de apoio à privacidade na Internet das Coisas
(Universidade Federal de Goiás, 2019-02-08) Arruda, Mayke Ferreira; Bulcão Neto, Renato de Freitas; http://lattes.cnpq.br/5627556088346425; Bulcão Neto, Renato de Freitas; Prazeres, Cássio Vinicius Serafim; Berardi, Rita Cristina Galarraga
In the Internet of Things (IoT) paradigm, real-world objects equipped with identification, detection, network, and processing capabilities communicate and consume services over the Internet to perform some task on behalf of users. Due to the growing popularization of devices with sensing capabilities and the consequent increase in data production from these devices, the literature states that the design of an ontology-based model is an essential starting point for addressing privacy risks in IoT, since the connected devices are increasingly able to monitor human activities. In addition, due to the complexity and dynamicity of IoT environments, we emphasize the need for privacy ontologies that combine expressive and extensible vocabulary but do not overload the processing of privacy data. Facing this problem, this work presents the development of an ontology-based solution for privacy in IoT, composed by: i) IoT-Priv, a privacy ontology for IoT, built as a light layer on IoT concepts imported from an emerging ontology, called IoTLite; and ii) IoT-PrivServ, a privacy management service, which provides functionalities for consumers and / or producers who use the IoT-Priv ontology in modeling their data, abstracting from them the complexity of perform such tasks. As contributions, the results of the evaluation of IoT-Priv and IoT-PrivServ indicate that we maintained the lightness characteristic present in IoT-Lite, which was one of our initial goals. In addition, we have demonstrated that IoT-Priv is expressive and extensible, since its concepts allow complex scenarios to be modeled, and if necessary, the extension points included in the ontology allow it to be imported and extended to meet more specific needs.
CGPlan: a scalable constructive path planning for mobile agents based on the compact genetic algorithm
(Universidade Federal de Goiás, 2017-02-16) Assis, Lucas da Silva; Laureano, Gustavo Teodoro; http://lattes.cnpq.br/4418446095942420; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Laureano, Gustavo Teodoro; Camilo Junior, Celso Gonçalves; Osório, Fernando Santos
between desired points. These optimal paths can be understood as trajectories that best achieves an objective, e.g. minimizing the distance travelled or the time spent. Most of usual path planning techniques assumes a complete and accurate environment model to generate optimal paths. But many of the real world problems are in the scope of Local Path Planning, i.e. working with partially known or unknown environments. Therefore, these applications are usually restricted to sub-optimal approaches which plan an initial path based on known information and then modifying the path locally or re-planning the entire path as the agent discovers new obstacles or environment features. Even though traditional path planning strategies have been widely used in partially known environments, their sub-optimal solutions becomes even worse when the size or resolution of the environment's representation scale up. Thus, in this work we present the CGPlan (Constructive Genetic Planning), a new evolutionary approach based on the Compact Genetic Algorithm (cGA) that pursue efficient path planning in known and unknown environments. The CGPlan was evaluated in simulated environments with increasing complexity and compared with common techniques used for path planning, such as the A*, the BUG2 algorithm, the RRT (Rapidly-Exploring Random Tree) and the evolutionary path planning based on classic Genetic Algorithm. The results shown a great efficient of the proposal and thus indicate a new reliable approach for path planning of mobile agents with limited computational power and real-time constraints on on-board hardware.
Algoritmos baseados em estratégia evolutiva para a seleção dinâmica de espectro em rádios cognitivos
(Universidade Federal de Goiás, 2013-11-22) Barbosa, Camila Soares; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Corrêa, Sand Luz; Camilo Junior, Celso Gonçalves; Santos, Aldri Luiz dos
One of the main challenges in Dynamic Spectrum Selection for Cognitive Radios is the choice of the frequency range for each transmission. This choice should minimize interference with legacy devices and maximize the discovering opportunities or white spaces. There are several solutions to this issue, and Reinforcement Learning algorithms are the most successful. Among them stands out the Q-Learning whose weak point is the parameterization, since adjustments are needed in order to reach successfully the proposed objective. In that sense, this work proposes an algorithm based on evolutionary strategy and presents the main characteristics adaptability to the environment and fewer parameters. Through simulation, the performance of the Q-Learning and the proposal of this work were compared in different scenarios. The results allowed to evaluate the spectral efficiency and the adaptability to the environment. The proposal of this work shows promising results in most scenarios.

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