La obtención de información a partir de un conjunto de datos o minería de datos es una tarea compleja que involucra varias etapas, tal como sucede en la minería de texto. Esta puede ser considerada como un caso particular de minería de datos donde los datos contemplan la incorporación de texto. Ambos procesos de minería se vuelven aun más complejos cuando nos encontramos ante grandes cúmulos de datos o texto. Es común encontrar conjuntos de datos grandes, complejos y ricos en información en áreas como medicina, comercio, ingeniería y ciencias de la computación. Simultáneamente, los avances tecnológicos han dado lugar a la acumulación de sustanciosas cantidades de documentos, artículos y texto; el ejemplo más contundente de esta clase de material es la Web, la cual se estima que alcanza más de 8.05 billones de páginas. La propuesta de esta tesis es el uso de herramientas evolutivas mono- y multi-objetivo como un soporte para algunas de las etapas de este proceso. En particular, las etapas que implican optimización y búsqueda dentro de estos grandes espacios en los cuales otros métodos serían inviables. A lo largo de la investigación se desarrollaron, evaluaron y compararon algoritmos evolutivos mono y multi-objetivo tanto para la rama de minería de datos como para la rama de minería de texto. Como caso particular dentro de minería de datos, se contempló el problema de encontrar las relaciones más relevantes entre variables dentro de distintos conjuntos de datos. Dichas relaciones, no son visibles para un experto cuando se encuentra frente a la base de datos original cruda, la cual puede contemplar miles de variables y miles de instan-cias. Para resolver este problema se propuso una metodología de dos fases. Los algoritmos desarrollados en este contexto se integraron a la primera fase de la arquitectura y fueron exitosamente utilizados como mecanismo de búsqueda masiva. Por otra parte, en el caso de minería de texto se abordó el problema de recuperar información relacionada y novedosa con respecto a un tópico de interés. Para este problema se propuso, implementó y evaluó una arquitectura que, partiendo de una descripción para el tópico de interés, evoluciona varios conjuntos de términos hacia conjuntos que logren obtener mejores documentos con respecto a dicho tema de interés y con respecto a los objetivos propuestos (por ejemplo: simi-litud, precisión, cobertura). Dentro de las técnicas evolutivas multi-objetivo propuestas, se diseñaron adaptaciones de los algoritmos basados en Pareto más prometedores reportados por la literatura y se propusieron versiones multi-objetivo agregativas. Ambos enfoques, los basados en Pareto y los agregativos, demostraron ser claramente competentes tanto para minería de datos como para minería de texto. / Data mining comprises the capture of information from data, which is a complex task that involves many stages. The same applies to text mining that can be considered as a special case of data mining where the data include text. As data and text sets increase, both mining processes become even more complicated. Large, complex and rich information data sets arise in many common research elds like medicine, commerce, engineering and computer science. Simultaneously, techno-logical advances have led to theaccumulation of substantial amounts of documents, articles and text; the clearest example of this kind of material is the Web, which is estimated to have reached more than 8.05 billion pages. This thesis proposes the use of mono- and multi-objective evolutionary tools as support in some of the stages of the data and text mining processes. In particular, those stages which imply optimiza-tion and search in wide search spaces where other methods could be unfeasible. In this research work, several mono- and multi-objective evolutionary algorithms were developed, evaluated and compared for both, data and text mining research areas. As a particular case in data mining, the problem of finding the most relevant relationship among variables from the data was considered. These relations, are not obvious for experts when they are faced with the original raw database, which can include thousands of variables and thousand of samples. In order to solve this problem, a two-phase methodology was proposed. In this context, the developed algorithms were integrated into the first phase and were succesfully used as massive search mechanisms. On the other hand, as a particular case of the text mining research area, the problem of retrieving novel material that is related to a search context was considered. In order to overcome this problem, an architecture was proposed, implemented and evaluated. Starting from a description for the topic of interest, this architecture evolves several sets of terms towards sets which can obtain better documents with respect to both, the topic of interest and the proposed objectives (e.g., similarity, precision, recall). Among the proposed multi-objetive evolutionary techniques, adap-tations of the more promising reported Pareto-based evolutionary algorithms were designed and new multi-objective aggregative schemes were proposed. Both approaches- i.e., the Pareto-based strategy and the aggregative techniques- proved to be clearly competent for both research areas: data and text mining.

Teaching simulated biped robots to walk is a popular problem in machine learning. However, until this thesis, evolving a biped controller has not been attempted through an indirect encoding, i.e. a compressed representation of the solution, despite the fact that natural bipeds such as humans evolved through such an indirect encoding (i.e. DNA). Thus the promise for indirect encoding is to evolve gaits that rival those seen in nature. In this thesis, an indirect encoding called HyperNEAT evolves a controller for a biped robot in a computer simulation. To most effectively explore the deceptive behavior space of biped walkers, novelty search is applied as a fitness metric. The result is that although the indirect encoding can evolve a stable bipedal gait, the overall neural architecture is brittle to small mutations. This result suggests that some capabilities might be necessary to include beyond indirect encoding, such as lifetime adaptation. Thus this thesis provides fresh insight into the requisite ingredients for the eventual achievement of fluid bipedal walking through artificial evolution.

Thesis (MSc)--University of Stellenbosch, 2007. / ENGLISH ABSTRACT: We use Evolutionary Multi-Objective Optimisation (EMOO) algorithms to optimise objective functions that reflect situations in communication networks. These include functions that optimise Network Engineering (NE) objective functions in core, metro and wireless sensor networks. The main contributions of this thesis are threefold. Routing and Wavelength Assignment (RWA) for IP backbone networks. Routing and Wavelength Assignment (RWA) is a problem that has been widely addressed by the optical research community. A recent interest in this problem has been raised by the need to achieve routing optimisation in the emerging generation multilayer networks where data networks are layered above a Dense Wavelength Division Multiplexing (DWDM) network. We formulate the RWA as both a single and a multi-objective optimisation problem which are solved using a two-step solution where (1) a set of paths are found using genetic optimisation and (2) a graph coloring approach is implemented to assign wavelengths to these paths. The experimental results from both optimisation scenarios reveal the impact of (1) the cost metric used which equivalently defines the fitness function (2) the algorithmic solution adopted and (3) the topology of the network on the performance achieved by the RWA procedure in terms of path quality and wavelength assignment. Optimisation of Arrayed Waveguide Grating (AWG) Metro Networks. An Arrayed Waveguide Grating (AWG) is a device that can be used as a multiplexer or demultiplexer in WDM systems. It can also be used as a drop-and-insert element or even a wavelength router. We take a closer look at how the hardware and software parameters of an AWG can be fine tuned in order to maximise throughput and minimise the delay. We adopt a multi-objective optimisation approach for multi-service AWG-based single hop metro WDM networks. Using a previously proposed multi-objective optimisation model as a benchmark, we propose several EMOO solutions and compare their efficiency by evaluating their impact on the performance achieved by the AWG optimisation process. Simulation reveals that (1) different EMOO algorithms can exhibit different performance patterns and (2) good network planning and operation solutions for a wide range of traffic scenarios can result from a well selected EMOO algorithm. Wireless Sensor Networks (WSNs) Topology (layout) Optimisation. WSNs have been used in a number of application areas to achieve vital functions in situations where humans cannot constantly be available for certain tasks such as in hostile areas like war zones, seismic sensing where continuous inspection and detection are needed, and many other applications such as environment monitoring, military operations and surveillance. Research and practice have shown that there is a need to optimise the topology (layout) of such sensors on the ground because the position on which they land may affect the sensing efficiency. We formulate the problem of layout optimisation as a multi-objective optimisation problem consisting of maximising both the coverage (area) and the lifetime of the wireless sensor network. We propose different algorithmic evolutionary multi-objective methods and compare their performance in terms of Pareto solutions. Simulations reveal that the Pareto solutions found lead to different performance patterns and types of layouts. / AFRIKAANSE OPSOMMING: Ons gebruik ”Evolutionary Multi-Objective Optimisation (EMOO)” algoritmes om teiken funksies, wat egte situasies in kommunikasie netwerke voorstel, te optimiseer. Hierdie sluit funksies in wat ”Network Engineering” teiken funksies in kern, metro en wireless sensor netwerke optimiseer. Die hoof doelwitte van hierdie tesis is dus drievuldig. RWA vir IP backbone netwerke ”Routing and Wavelength Assignment (RWA)” is ’n probleem wat al menigte kere in die optiese navorsings kringe aangespreek is. Belangstelling in hierdie veld het onlangs ontstaan a.g.v. die aanvraag na die optimisering van routering in die opkomende generasie van veelvuldige vlak netwerke waar data netwerke in ’n vlak ho¨er as ’n ”Dense Wavelength Division Multiplexing (DWDM)” netwerk gele is. Ons formuleer die RWA as beide ’n enkele and veelvuldige teiken optimiserings probleem wat opgelos word deur ’n 2-stap oplossing waar (1) ’n stel roetes gevind word deur genetiese optimisering te gebruik en (2) ’n grafiek kleuring benadering geimplementeer word om golflengtes aan hierdie roetes toe te ken. Die eksperimentele resultate van beide optimiserings gevalle vertoon die impak van (1) die koste on wat gebruik word wat die ekwalente fitness funksie definieer , (2) die algoritmiese oplossing wat gebruik word en (3) die topologie van die netwerk op die werkverrigting van die RWA prosedure i.t.v. roete kwaliteit en golflengte toekenning. Optimisering van AWG Metro netwerk ’n ”Arrayed Waveguide Grating (AWG)” is ’n toestel wat gebruik kan word as ’n multipleksor of demultipleksor in WDM sisteme. Dit kan ook gebruik word as ’n val-en-inplaas element of selfs ’n golflengte router. Kennis word ingestel na hoe die hardeware en sagteware parameters van ’n AWG ingestel kan word om die deurset tempo te maksimeer en vertragings te minimiseer. Ons neem ’n multi-teiken optimiserings benadering vir multi diens, AWG gebaseerde, enkel skakel, metro WDM netwerke aan. Deur ’n vooraf voorgestelde multi teiken optimiserings model as ”benchmark” te gebruik, stel ons ’n aantal EMOO oplossings voor en vergelyk ons hul effektiwiteit deur hul impak op die werkverrigting wat deur die AWG optimiserings proses bereik kan word, te vergelyk. Simulasie modelle wys dat (1) verskillende EMOO algoritmes verskillende werkverrigtings patrone kan vertoon en (2) dat goeie netwerk beplanning en werking oplossings vir ’n wye verskeidenheid van verkeer gevalle kan plaasvind a.g.v ’n EMOO algoritme wat reg gekies word. ”Wireless Sensor Network” Topologie Optimisering WSNs is al gebruik om belangrike funksies te verrig in ’n aantal toepassings waar menslike beheer nie konstant beskikbaar is nie, of kan wees nie. Voorbeelde van sulke gevalle is oorlog gebiede, seismiese metings waar aaneenlopende inspeksie en meting nodig is, omgewings meting, militˆere operasies en bewaking. Navorsing en praktiese toepassing het getoon dat daar ’n aanvraag na die optimisering van die topologie van sulke sensors is, gebaseer op gronde van die feit dat die posisie waar die sensor beland, die effektiwiteit van die sensor kan affekteer. Ons formuleer die probleem van uitleg optimisering as ’n veelvuldige vlak optimiserings probleem wat bestaan uit die maksimering van beide die bedekkings area en die leeftyd van die wireless sensor netwerk. Ons stel verskillende algoritmiese, evolutionˆere, veelvuldige vlak oplossings voor en vergelyk hul werkverrigting i.t.v Pareto oplossings. Simulasie modelle wys dat die Pareto oplossings wat gevind word lei na verskillende werkverrigtings patrone en uitleg tipes.

Submitted in fulfillment of the requirements of the degree of Doctor of Engineering: Civil Engineering, Durban University of Technology. Durban. South Africa, 2015. / The planning and management of water resources systems often involve formulation and establishment of optimal operating policies and the study of trade-off between different objectives. Due to the intricate nature of water resources management tasks, several models with varying degrees of complexities have been developed and applied for resolving water resources optimisation and allocation problems. Nevertheless, there still exist uncertainties about finding a generally consistent and trustworthy method that can find solutions which are very close to the global optimum in all scenarios. This study presents the development and application of a new evolutionary multi-objective optimisation algorithm, combined Pareto multi-objective differential evolution (CPMDE). The algorithm combines methods of Pareto ranking and Pareto dominance selections to implement a novel generational selection scheme. The new scheme provides a systematic approach for controlling elitism of the population which results in the simultaneous creation of short solution vectors that are suitable for local search and long vectors suitable for global search. By incorporating combined Pareto procedures, CPMDE is able to adaptively balance exploitation of non-dominated solutions found with exploration of the search space. Thus, it is able to escape all local optima and converge to the global Pareto-optimal front. The performance of CPMDE was compared with 14 state-of-the-art evolutionary multi-objective optimisation algorithms. A total of ten test problems and three real world problems were considered in the benchmark of the algorithm. Findings suggest that the new algorithm presents an improvement in convergence to global Pareto-optimal fronts especially on deceptive multi-modal functions where CPMDE clearly outperformed all other algorithms in convergence and diversity. The convergence metric on this problem was several orders of magnitude better than those of the other algorithms. Competitive results obtained from the benchmark of CPMDE suggest that it is a good alternative for solving real multi-objective optimisation problems. Also, values of a variance statistics further indicate that CPMDE is reliable and stable in finding solutions and converging to Pareto-optimal fronts in multi-objective optimisation problems. CPMDE was applied to resolve water allocation problems in the Orange River catchment in South Africa. Results obtained from the applications of CPMDE suggest it represents an improvement over some existing methods. CPMDE was applied to resolve water allocation problems in the agricultural and power sectors in South Africa. These sectors are strategic in forging economic growth, sustaining technological developments and contributing further to the overall development of the nation. They are also germane in capacitating the South African government’s commitment towards equity and poverty eradication and ensuring food security. Harnessing more hydropower from existing water sources within the frontier of the country is germane in capacitating the South African Government’s commitment to reduction of the countries’ greenhouse gas emissions and transition to a low-carbon economy while meeting a national target of 3 725 megawatts by 2030. Application of CPMDE algorithm in the behavioural analysis of the Vanderkloof reservoir showed an increase of 20 310 MWH in energy generation corresponding to a 3.2 percent increase. On analysis of storage trajectories over the operating period, it was found that the real time analysis incorporating a hybrid between CPMDE and ANN offers a procedure with a high ability to minimize deviation from target storage under the prevailing water stress condition. Overall, the real time analysis provides an improvement of 49.32 percent over the current practice. Further analysis involving starting the simulation with a proposed higher storage volume suggests that 728.53 GWH of annual energy may be generated from the reservoir under medium flow condition without system failure as opposed to 629 GWH produced from current practice. This corresponds to a 13.66 percent increase in energy generation. It was however noted that the water resources of the dam is not in excess. The water in the dam is just enough to meet all current demands. This calls for proper management policies for future operation of the reservoir to guard against excessive storage depletions. The study herein also involved the development of a decision support system for the daily operation of the Vanderkloof reservoir. This provides a low cost solution methodology suitable for the sustainable operation of the Vanderkloof dam in South Africa. Adopting real time optimisation strategies may be beneficial to the operation of reservoirs. Findings from the study herein indicate that the new algorithm represents an improvement over existing methods. Therefore, CPMDE presents a new tool that nations can adapt for the proper management of water resources towards the overall prosperity of their populace. / D

The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.

[pt] Desde que foram propostos como método de otimização, os algoritmos evolutivos têm sido usados com sucesso para resolver problemas complexos nas mais diversas áreas como, por exemplo, o projeto automático de circuitos e equipamentos, planejamento de tarefas, engenharia de software e mineração de dados, entre tantos outros. Este sucesso se deve, entre outras coisas, ao fato desta classe de algoritmos não necessitar de formulações matemáticas rigorosas a respeito do problema que se deseja otimizar, além de oferecer um alto grau de paralelismo no processo de busca. No entanto, alguns problemas são computacionalmente custosos no que diz respeito à avaliação das soluções durante o processo de busca, tornando a otimização por algoritmos evolutivos um processo lento para situações onde se deseja uma resposta rápida do algoritmo (como por exemplo, problemas de otimização online). Diversas maneiras de se contornar este problema, através da aceleração da convergência para boas soluções, foram propostas, entre as quais destacam-se os Algoritmos Culturais e os Algoritmos Co-Evolutivos. No entanto, estes algoritmos ainda têm a necessidade de avaliar muitas soluções a cada etapa do processo de otimização. Em problemas onde esta avaliação é computacionalmente custosa, a otimização pode levar um tempo proibitivo para alcançar soluções ótimas. Este trabalho propõe um novo algoritmo evolutivo para problemas de otimização numérica (Algoritmo Evolutivo com Inspiração Quântica usando Representação Real - AEIQ- R), inspirado no conceito de múltiplos universos da física quântica, que permite realizar o processo de otimização com um menor número de avaliações de soluções. O trabalho apresenta a modelagem deste algoritmo para a solução de problemas benchmark de otimização numérica, assim como no treinamento de redes neurais recorrentes em problemas de aprendizado supervisionado de séries temporais e em aprendizado por reforço em tarefas de controle. Os resultados obtidos demonstram a eficiência desse algoritmo na solução destes tipos de problemas. / [en] Since they were proposed as an optimization method, the evolutionary algorithms have been successfully used for solving complex problems in several areas such as, for example, the automatic design of electronic circuits and equipments, task planning and scheduling, software engineering and data mining, among many others. This success is due, among many other things, to the fact that this class of algorithms does not need rigorous mathematical formulations regarding the problem to be optimized, and also because it offers a high degree of parallelism in the search process. However, some problems are computationally intensive when it concerns the evaluation of solutions during the search process, making the optimization by evolutionary algorithms a slow process for situations where a quick response from the algorithm is desired (for instance, in online optimization problems). Several ways to overcome this problem, by speeding up convergence time, were proposed, including Cultural Algorithms and Coevolutionary Algorithms. However, these algorithms still have the need to evaluate many solutions on each step of the optimization process. In problems where this evaluation is computationally expensive, the optimization might take a prohibitive time to reach optimal solutions. This work proposes a new evolutionary algorithm for numerical optimization problems (Quantum- Inspired Evolutionary Algorithm for Problems based on Numerical Representation - QIEA-R), inspired in the concept of quantum superposition, which allows the optimization process to be carried on with a smaller number of evaluations. The work presents the modelling for this algorithm for solving benchmark numerical optimization problems, and for training recurrent neural networks in supervised learning and reinforcement learning. The results show the good performance of this algorithm in solving these kinds of problems.

[pt] A atividade de gerenciamento de reservatórios é uma tarefa essencial que visa o desafio da otimização da explotação de reservatórios de petróleo. Como resposta a tal desafio a indústria de óleo e gás vem desenvolvendo novas tecnologias, como a de poços inteligentes. Esses poços tem objetivo de baratear as operações de restaurações mais corriqueiras através do controle de sua tecnologia. Assim, este trabalho trata do desenvolvimento de campos inteligentes e apresenta um sistema de apoio à decisão capaz de otimizar, através de algoritmos evolucionários, o processo de controle da tecnologia de poços inteligentes considerando incertezas de falha e geológica. Além disso, o sistema se propõe a apoiar na tomada de decisão pelo uso ou não de poços inteligentes, dado um reservatório pronto para ser explorado ou para receber investimentos de expansão. O controle da tecnologia de poços inteligentes (IWT - Intelligent Wells Technology) empregado nesse estudo, refere-se à operação de abertura e fechamento dos dispositivos (válvulas) existentes nesses tipos de poços. Através da otimização com algoritmos genéticos se busca uma estratégia de controle pró-ativo, em outras palavras, agir antes do efeito, onde se busca nos tempos iniciais de produção uma configuração de válvulas que seja capaz de: atrasar a chegada da frente de água aos poços produtores, antecipar a produção de óleo ou melhorar a recuperação de óleo do campo; em conseqüência, uma operação que leve à maximização do valor presente líquido (VPL). O emprego de estratégias de controle que visam beneficiar a completação identifica o campo como inteligente. Outros trabalhos abordam o problema de otimização de controle de válvulas em poços inteligentes, porém eles utilizam métodos clássicos de otimização que limitam o número de válvulas ou ainda otimizam estratégias sem considerar os intervalos de tempo desejados para manutenção das válvulas. O modelo evolucionário empregado nesse estudo, baseado em algoritmos genéticos, consegue formular uma estratégia de controle para todas as válvulas presentes em uma determinada configuração de produção, em qualquer intervalo de tempo desejado, atendendo ao critério econômico de maximizar o VPL. Para apoiar a tomada de decisão, pelo uso ou não de poços inteligentes, consideram-se incertezas de falha e geológica. O modelo proposto foi avaliado em três reservatórios petrolíferos, sendo o primeiro um reservatório sintético, e os outros dois reservatórios mais complexos com características mais próximas das reais. Os resultados encontrados indicam que o modelo proposto permite alcançar boas estratégias de controle que levam a um aumento do VPL. A principal contribuição deste trabalho é a concepção e implementação de um sistema baseado em técnicas inteligentes capaz de apoiar no desenvolvimento e gerenciamento de reservatórios petrolíferos inteligentes considerando incertezas. / [en] The reservoir management is an important task that aims at the optimization of oil reservoir exploitation. To support this challenging mission, the oil and gas industry has been developing new technologies such as intelligent wells. The purpose of these wells is to reduce costs of the most common restoring operations by control of their actuators. Thus, this work deals with intelligent fields development and presents a decision support system able to optimize, by using evolutionary algorithms, the intelligent wells technology control process considering geological and technical uncertainties. In addition, the system gives support for the decision of rather to use or not intelligent wells, given a reservoir ready to be explored or to receive expansion investments. The control of Intelligent Wells Technology (IWT), as applied in this study, refers to the opening and closing operations of valves in these types of wells. An optimization based on genetic algorithms is used to produce a pro-active control strategy, that is, one that anticipates the actions to be taken in present time in order to achieve better results in the future. Such a strategy proposes a valve configuration that will be able to: delay the water cut on producer wells, advance the oil production or benefit the oil recuperation. As a result, the obtained configuration leads to a maximization of the NPV (Net Present Value). The usage of control strategies that aim to benefit completion identifies the oil field as intelligent. Other works also deal with valve control optimization problems in intelligent wells. However, they use classical optimization methods; these methods limit the number of valves or optimize strategies without considering time. The evolutionary model, based on genetic algorithm, applied in this study, can formulate a control strategy for all valves in a certain production configuration, for any desired time interval, according to the economical criteria of NPV maximization. In order to support the decision making for the use or not of intelligent wells, technical and geological uncertainties are considered. The proposed model was evaluated in three oil reservoirs. The first one is a synthetic reservoir, simple and not real; the other two are more complex with close to real characteristics. The results obtained indicate that the proposed model allows good control strategies that increase the NPV. The main contribution of this work is the conception and implementation of a system based on intelligent techniques that is able to support the development and management of intelligent oil reservoirs considering uncertainties.

No contexto da engenharia de produção a pesquisa analisa a coordenação do processo de produção. As principais frentes de gerentes de planejamento e de controladoria são o atendimento à demanda do mercado e os custos de produção divergentes em relação ao plano inicial. Verifica-se que há uma lacuna processual entre dois processos organizacionais, a análise de controladoria direcionada a custos realizados e a análise de planejamento e controle da produção voltada ao atendimento da demanda. A assimetria do uso do mesmo conjunto de dados com visões críticas distintas, contudo com intenções finais similares, motivam o trabalho. Levanta-se a hipótese da análise dos dados com fundamentação conceitual estruturada para abranger a rede de colaboração produtiva. O objetivo é, então, estabelecer um método de análise para a coordenação do processo de produção, elaborado sob a ótica de redes de inovação colaborativas apoiado por agente inteligente evolutivo. A fundamentação conceitual da coordenação do processo de produção, da inovação e da função de produção compõem a estrutura da revisão literária. Pelo método de pesquisa, sob o recorte analítico de vertentes teóricas de redes, uma inovação promovida pela pesquisa foi o mapeamento e a associação de variáveis processuais internas ao nó principal da rede aos nós processuais externos. Uma aplicação tecnológica comercial não é suficiente para interpretar esse ambiente dinâmico e orientado à mudança. O algoritmo DAMICORE, sob a égide evolutiva da biologia, encontra nós homólogos e interpreta o rótulo dos nós processuais validados em campo. O novo método de análise para a coordenação do processo de produção é aferido em rede por um projeto piloto e replicado, então, em vinte e uma redes com resultados melhores comparativamente ao método tradicional. Assim, a pesquisa cria um novo paradigma de análise para processos em rede e demonstra a representatividade de variáveis associadas a nós processuais, desde que eleitas conceitualmente e validadas por especialistas da área. / In the industrial engineering context this research examines the coordination of production process. The main concerns of planning managers and controllers are the demand oscillation and deviation of budget production costs. There is a lack between two processes, the controlling analysis aimed at real costs and planning analysis aimed at demand. The motivation of this work is the asymmetry of the use of the same data set from different perspectives but with similar goals. It is possible to elaborate the hypothesis to analyse of structured data with the conceptual basis to study the cooperative network. The goal is to establish a method of analysis for the coordination of production process systematized from the perspective of innovation collaborative networks where this method is compiled by evolutionary concepts with an intelligent agent application. The literature review comprises the coordination of production process, the innovation concepts and the production function concepts. The method of research applies variables belonging to internal process to external process from principal network node, this approach it is done under the analytical of theoretical networks basis. The method of research is designed to find variables belonging to internal process to relate to external process variables from principal network node, this approach it is done under the analytical of theoretical networks basis. This way has promoted a innovation for the work. A commercial technological application is not enough to mining data set from this dynamic and change oriented environment. The DAMICORE algorithm under the evolutionary concepts from biology area can find correlated nodes validated with the field data. The new method of analysis for the coordination of production process is adjusted by a pilot project then it is replicated in twenty-one networks with amazing results when compared to the current method. The research creates a new paradigm for process analysis and demonstrates the variables power representation and association from network processes if they are under conceptual basis to validate by experts.

[pt] Síntese de Sistemas Fuzzy por Computação Evolucionária propõe uma metodologia de projeto para o desenvolvimento de sistemas fuzzy fundamentada em técnicas de computação evolucionária. Esta metodologia contempla as etapas de concepção do sistema fuzzy e a implementação em hardware do circuito eletrônico que o representa. A concepção do sistema é realizada num ambiente de projeto no qual sua base de conhecimento - composta da base de regras e demais parâmetros característicos - é evoluída, por intermédio de simulação, através do emprego de um novo algoritmo de três estágios que utiliza Algoritmos Genéticos. Esta estratégia enfatiza a interpretabilidade e torna a criação do sistema fuzzy mais simples e eficiente para o projetista, especialmente quando comparada com o tradicional ajuste por tentativa e erro. A implementação em hardware do circuito é realizada em plataforma de desenvolvimento baseada em Eletrônica Evolucionária. Um conjunto de circuitos, denominados de blocos funcionais, foi desenvolvido e evoluído com sucesso para viabilizar a construção da estrutura final do sistema fuzzy. / [en] Synthesis of Fuzzy Systems through Evolutionary Computation proposes a methodology for the design of fuzzy systems based on evolutionary computation techniques. A three-stage evolutionary algorithm that uses Genetic Algorithms (GAs) evolves the knowledge base of a fuzzy system - rule base and parameters. The evolutionary aspect makes the design simpler and more efficient, especially when compared with traditional trial and error methods. The method emphasizes interpretability so that the resulting strategy is clearly stated. An Evolvable Hardware (EHW) platform for the synthesis of analog electronic circuits is proposed. This platform, which can be used for the implementation of the designed fuzzy system, is based on a Field Programmable Analog Array (FPAA). A set of evolved circuits called functional blocks allows the implementation of the fuzzy system.

No contexto da engenharia de produção a pesquisa analisa a coordenação do processo de produção. As principais frentes de gerentes de planejamento e de controladoria são o atendimento à demanda do mercado e os custos de produção divergentes em relação ao plano inicial. Verifica-se que há uma lacuna processual entre dois processos organizacionais, a análise de controladoria direcionada a custos realizados e a análise de planejamento e controle da produção voltada ao atendimento da demanda. A assimetria do uso do mesmo conjunto de dados com visões críticas distintas, contudo com intenções finais similares, motivam o trabalho. Levanta-se a hipótese da análise dos dados com fundamentação conceitual estruturada para abranger a rede de colaboração produtiva. O objetivo é, então, estabelecer um método de análise para a coordenação do processo de produção, elaborado sob a ótica de redes de inovação colaborativas apoiado por agente inteligente evolutivo. A fundamentação conceitual da coordenação do processo de produção, da inovação e da função de produção compõem a estrutura da revisão literária. Pelo método de pesquisa, sob o recorte analítico de vertentes teóricas de redes, uma inovação promovida pela pesquisa foi o mapeamento e a associação de variáveis processuais internas ao nó principal da rede aos nós processuais externos. Uma aplicação tecnológica comercial não é suficiente para interpretar esse ambiente dinâmico e orientado à mudança. O algoritmo DAMICORE, sob a égide evolutiva da biologia, encontra nós homólogos e interpreta o rótulo dos nós processuais validados em campo. O novo método de análise para a coordenação do processo de produção é aferido em rede por um projeto piloto e replicado, então, em vinte e uma redes com resultados melhores comparativamente ao método tradicional. Assim, a pesquisa cria um novo paradigma de análise para processos em rede e demonstra a representatividade de variáveis associadas a nós processuais, desde que eleitas conceitualmente e validadas por especialistas da área. / In the industrial engineering context this research examines the coordination of production process. The main concerns of planning managers and controllers are the demand oscillation and deviation of budget production costs. There is a lack between two processes, the controlling analysis aimed at real costs and planning analysis aimed at demand. The motivation of this work is the asymmetry of the use of the same data set from different perspectives but with similar goals. It is possible to elaborate the hypothesis to analyse of structured data with the conceptual basis to study the cooperative network. The goal is to establish a method of analysis for the coordination of production process systematized from the perspective of innovation collaborative networks where this method is compiled by evolutionary concepts with an intelligent agent application. The literature review comprises the coordination of production process, the innovation concepts and the production function concepts. The method of research applies variables belonging to internal process to external process from principal network node, this approach it is done under the analytical of theoretical networks basis. The method of research is designed to find variables belonging to internal process to relate to external process variables from principal network node, this approach it is done under the analytical of theoretical networks basis. This way has promoted a innovation for the work. A commercial technological application is not enough to mining data set from this dynamic and change oriented environment. The DAMICORE algorithm under the evolutionary concepts from biology area can find correlated nodes validated with the field data. The new method of analysis for the coordination of production process is adjusted by a pilot project then it is replicated in twenty-one networks with amazing results when compared to the current method. The research creates a new paradigm for process analysis and demonstrates the variables power representation and association from network processes if they are under conceptual basis to validate by experts.