Over time, Brazilian citizens have suffered with the recurring increases in fuel prices based on international price parity. The price fluctuation indirectly increases the ICMS tax - Tax on Operations related to the Circulation of Goods and on the Provision of Interstate and Intercity Transport and Communication Services - which is a tax revenues from states, considered their main source of revenues. If on one side, it benefits states, on the other, it negatively impacts the taxpayers and undermines some services, thus becoming a reverse problem, in which products and services are no longer sold or are replaced by lower-value products and consequently diminishing the state’s revenues. Therefore, the fluctuations in fuel final prices impact several production chains that directly influence the state’s revenues. Allied to this theme, there are frequent discussions about the change in the Brazilian taxation policy, which directly impacts the states’ revenues. However, little is discussed about the direct and indirect revenue projections caused by potential tax changes. In this context, this study aims to develop a type of ICMS tax collection simulator in the state of Rio de Janeiro. The developed tool uses public and historical fuel price data together with several deep learning models to generate an ICMS collection forecasting system. In this way, it is possible to modify fuel prices in order to predict potential impacts on ICMS tax collection. In this way, using the tool, it is possible to avoid a sudden drop in revenue, even from a tax increase. This study also highlighted the relationship among the collected revenues, the fuel’s price and consumption and the local economy activity indicator. Moreover it showed that keeping the fuel prices around a stable value over a year period results in no difference on state collection.

Seismic fault segmentation plays a crucial role in geophysics, especially in the exploration and extraction of oil and natural gas. However, identifying faults from seismic data is challenging and time-consuming due to the large volumes of data, complexity, noise, ambiguity, and reliance on subjective manual identification by specialists. To address these challenges, various automated methods based on Deep Learning have been proposed, but they require substantial amounts of labeled data. Synthetic data generation has been explored as a solution to generalize models for new real data, but it faces the domain shift problem, where different datasets may have different distributions and behaviour. In this paper, we propose a novel approach using Unsupervised Domain Adaptation (UDA) techniques to mitigate the domain shift issue in seismic fault detection. We utilize a synthetic dataset known for its use in the FaultSeg3D model and a labeled real dataset called Thebe. The selected techniques include Maximum Mean Discrepancy (MMD) and Fourier Domain Adaptation (FDA). MMD aims to reduce the discrepancy between internal layer outputs in the neural network (feature-level), while FDA transfers features between input images (image-level). For the experiments, we employed a smaller version of UNet and a variant called Atrous UNet, which has shown improvements in seismic images by utilizing Dilated Convolutional layers. The results demonstrated up to a 12% improvement in Intersection over Union and a 10% improvement in F1 by applying UDA techniques. Moreover, the proposed approach achieved more continuous and fewer false positive seismic fault detections.

The occurrence of a disaster brings about damages, destruction, ecological disruption, loss of human life, human suffering, deterioration of health and health service of sufficient magnitude to require external assistance, demanding the mobilization and deployment of emergency rescue units within the affected area, in order to reduce casualties and economic losses. The scheduling of those units is one of the key issues in the emergency response phase and can be seen as a generalization of the unrelated parallel machine scheduling problem with sequence and machine dependent setup. The objective is to minimize the total weighted completion time of the incidents to be attended, where the weight correspond to its severity level. We propose a biased random-key genetic algorithm to tackle this problem, considering fuzzy required processing times for the incidents, and compare the solutions with those generated by a constructive heuristic, from the literature, developed to deal with this problem. Our results show that the genetic algorithm's solutions are 2.17% better than those obtained with the constructive heuristic when applied to instances with up to 40 incidents and 40 rescue units.

Oil exploitation and production fields require allocating large investments to reduce low production-associated risks, which can be minimized by the successful characterization of oil reservoirs. The characterization process lies on geological property estimates generated during well-drilling procedures and on information extracted from 3D seismic data. Computational intelligence techniques proved to be efficient tools to estimate nonlinear relations, which can be applied to predict reservoir parameters. The aim of the current study is to address an approach based on the application of the Multi-Gene Genetic Programming (mgGP) algorithm to estimate porosity in an oil reservoir by using seismic data and well logs. The relation between seismic and porosity data about Namorado oil field was satisfactorily represented by means of mgGP.

This paper presents the research and development of a hybrid neuro-fuzzy model for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-GC). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

This article tackles the problem of natural gas sensors allocation in oil platforms. A mathematical formulation of the problem is developed and used in the creation of a genetic algorithm whose goals are to maximize the probability of detection and, secondarily, minimize the number of sensors installed.

This paper proposes a new approach to Particle Swarm Optimization (PSO) to solve nonlinear problems with linear and nonlinear constraints. A crossover operator and a new particle updating method, named Footholds Concept, were developed to guarantee fully feasible solutions and better search-space coverage, respectively. In addition, a novel swarm initialization heuristic is applied to benchmarks with equality constraints. The algorithm has been tested on 13 common benchmark functions. Experimental results show that it is very competitive as it increases PSO efficiency and improves convergence speed.

This paper investigates the potential of evolutionary algorithms, developed by the combination of genetic programming (GP) and automatic differentiation methods (AD), in determining analytic solutions to ordinary and partial differential equations (ODE and PDE). In turn, AD is a set of techniques based on the mechanical application of the chain rule to numerically evaluate the derivative of a function specified by a computer program. The AD method has a fundamental role in this work since it calculates the exact values of the derivatives of a function for a given set of input values while numerical differentiation methods introduce unacceptable round-off errors in the discretization process. With this purpose, and using the Matlab programming environment, we developed several algorithms (namely GPAD) and addressed problems of different kinds of differential equations. The results are promising, with exact solutions obtained for most of the addressed problems, which include equations where not even commercial systems could find a symbolic solution. These results empirically indicate that GPAD can be an efficient and robust methodology to find analytic solutions for ODE and PDE.

One of the most important issues in oil \& gas industry is the lithological identification. Lithology is the macroscopic description of the physical characteristics of a rock. This work proposes a new methodology for lithological discrimination, using GPF-CLASS model (Genetic Programming for Fuzzy Classification) a Genetic Fuzzy System based on Multi-Gene Genetic Programming. The main advantage of our approach is the possibility to identify, through seismic patterns, the rock types in new regions without requiring opening wells. Thus, we seek for a reliable model that provides two flexibilities for the experts: evaluate the membership degree of a seismic pattern to the several rock types and the chance to analyze at linguistic level the model output. Therefore, the final tool must afford knowledge discovery and support to the decision maker. Also, we evaluate other 7 classification models (from statistics and computational intelligence), using a database from a well located in Brazilian coast. The results demonstrate the potentialities of GPF-CLASS model when comparing to other classifiers.

The superior performance of quantum computers in some problems lies in the direct use of quantum mechanics phenomena. This ability has originated the quantum-inspired evolutionary algorithms (QIEAs), which are classical algorithms (for classical computers) that exploit quantum mechanics principles to improve their performance. Several proposed QIEAs are able to outperform their traditional counterparts when applied to different kinds of problems. Aiming to exploit this new paradigm on genetic programming (GP), this paper introduces a novel QIEA model (quantum-inspired linear GP—QuaLiGP), which evolves machine code programs. QuaLiGP is inspired on multi-level quantum systems, and its operation is based on quantum individuals, which represent a superposition of all programs (solutions) of the search space. The tests use symbolic regression and binary classification as knowledge management problems to assess the QuaLiGP performance and compare it with Automatic Induction of Machine Code by Genetic Programming model, which is currently the most efficient GP model to evolve machine code. Results show that QuaLiGP outperforms the reference GP system for all these problems, by achieving better solutions from a smaller number of evaluations and by using fewer parameters and operators. This paper concludes that the quantum-inspired paradigm can be a competitive approach to evolve programs efficiently, encouraging improvements and extensions of QuaLiGP.

Quantum-inspired evolutionary algorithms (QIEAs) exploit principles of quantum mechanics to improve the performance of classical evolutionary algorithms. This paper describes the latest version of a QIEA model (“Quantum-Inspired Linear Genetic Programming” - QILGP) to evolve machine code programs. QILGP is inspired on multilevel quantum systems and its operation is based on quantum individuals, which represent a superposition of all programs of search space (solutions). Symbolic regression problems and the current more efficient model to evolve machine code (AIMGP) are used in comparative tests, which aim to evaluate the performance impact of introducing demes (subpopulations) and a limited migration strategy in this version of QILGP. It outperforms AIMGP by obtaining better solutions with fewer parameters and operators. The performance improvement achieved by this latest version of QILGP encourages its ongoing and future enhancements. Thus, this paper concludes that the quantum inspiration paradigm can be a competitive approach to evolve programs more efficiently.

In this work we present two evolutionary algorithms applied to look for positive water cluster structures. Both algorithms were applied in order to simulate the formation of the aggregates using the neutral clusters as a precursor. In other words, we are not looking for the global minima positive water cluster structures, but rather than trying to find the most stable structures formed from neutral stable clusters. To achieve our goal three steps were executed. In the first one we looked for the most stable structures for (H2O)n(n = 2 - 8), applying a genetic algorithm. In the second step we simulated that the found neutral clusters had lost one of their electrons, creating positive clusters (H2O)+/n. Finally, in the last step we simulated the creation of positive cluster by the aggregation of one positive ion, forming (H2O)nH2O+ clusters. in the latter stage we applied a quantum inspired evolutionary algorithm for numerical optimization (QIEA-R). Results of our search present innovative positive water structures and was able to compare two different ways for ionic cluster formation.

In this work we present a parallel evolutionary algorithm applied to the search of the lowest-energy structures of heterogeneous atomic clusters. A new and improved crossover operator is proposed in order to always ensure the creation of new clusters with the same number of atomic elements. The approach proposed proved to be efficient and fast as all cluster calculations were performed by an ab initio quantum mechanics method, which is computationally expensive. Results of our search, obtained using the proposed approach, have been compared with previous calculations, and the efficiency has been confirmed, as we were able to find the global minimum and propose a wide number of new isomers of low-energy. Specifically, we addressed the problem to deal with clusters of lithium and fluorine atoms. However, the proposed algorithm can be extended to all kind of atomic and molecular clusters.

Composite materials have changed the way of using polymers, as the strength was favored by the incorporation of fibers and particles. This new class of materials allowed a larger number of applications. The insertion of nanometric sized particles has enhanced the variation of properties with a smaller load of fillers. In this paper, we attempt to a better understanding of nanocomposites by using an artificial intelligence's technique, known as artificial neural networks. This technique allowed the modeling of Young's modulus of nanocomposites. A good approximation was obtained, as the correlation between the data and the response of the network was high, and the error percentage was low.

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. 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 can take a prohibitive time to reach optimal solutions. This work presents an 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. It extends previous works by presenting a broader range of tests and improvements on the algorithm. The results show the good performance of this algorithm in solving numerical problems.

The huge performance superiority of quantum computers for some specific problems lies in their direct use of quantum mechanical phenomena (e.g. superposition of states) to perform computations. This has motivated the creation of quantum-inspired evolutionary algorithms (QIEAs), which successfully use some quantum physics principles to improve the performance of evolutionary algorithms (EAs) for classical computers. This paper proposes a novel QIEA (Quantum-Inspired Linear Genetic Programming - QILGP) for automatic synthesis of machine code (MC) programs and aims to present a preliminary evaluation of applying the quantum-inspiration paradigm to evolve programs by using two symbolic regression problems. QILGP performance is compared to AIMGP model, since it is the most successful genetic programming technique to evolve MC. In the first problem, the hit ratio of QILGP (100%) is greater than the one of AIMGP (77%). In the second problem, QILGP seems to carry on a less greedy search than AIMGP. Since QILGP presents some satisfactory results, this paper shows that the quantum-inspiration paradigm can be a competitive approach to evolve programs more efficiently, which encourages further developments of that first and simplest QILGP model with multiple individuals.

The volume of energy loss that Brazilian electric utilities have to deal with has been ever increasing. The electricity concessionaries are suffering significant and increasing loss in the last years, due to theft, measurement errors and many other kinds of irregularities. Therefore, there is a great concern from those companies to identify the profile of irregular customers, in order to reduce the volume of such losses. This paper presents the proposal of an intelligent system, composed of two neural networks ensembles, which intends to increase the level of accuracy in the identification of irregularities among low tension consumers. The data used to test the proposed system are from Light S.A. Company, the Rio de Janeiro concessionary. The results obtained presented a significant increase in the identification of irregular customers when compared to the current methodology employed by the company.

CMOS technology miniaturization limits have promoted research on new alternatives which can keep the technologically advanced level of the last decades. Quantum-dot Cellular Automata (QCA) is a new technology in the nanometer scale that has been considered as one of these alternatives. QCA have a large potential in the development of circuits with high space density and low heat dissipation and allow the development of faster computers with lower power consumption. Differently from conventional technologies, QCA do not codify information by means of electric current flow, but rather by the configuration of electrical charges in the interior of the cells. The Coulomb interaction between cells is responsible for the flow of information. This paper proposes the use of computational intelligence techniques in the simulation and in the automatic synthesis of QCA circuits. The first results show that these techniques may play an important role in this research area since they are capable of simulating efficiently and fast, synthesizing optimized circuits with a reduced number of cells. Such optimization reduces the possibility of failures and guarantees higher speed.

Since they were proposed as an optimization method, evolutionary algorithms(EA) have been used to solve problems in several research fields. This success is due, besides other things, to the fact that these algorithms do not require previous considerations regarding the problem to be optimized and offers a high degree of parallelism. However, some problems are computationally intensive regarding solution’s evaluation, which makes the optimization by EA’s slow for some situations. This paper proposes a novel EA for numerical optimiza tion inspired by the multiple universes principle of quantum computing. Results show that this algorithm can find better solutions, with less evaluations, when compared with similar algorithms.

This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study.

This work presents an extension of the hybrid reinforcement learning-hierarchical neuro-fuzzy politree model (RL-HNFP) and presents its performance in a multi-obstacle environment. The main objective of the RL-HNFP model is to provide an agent with intelligence, making it capable, by interacting with its environment, to acquire and retain knowledge for reasoning (infer an action). The original RL-HNFP applies hierarchical partitioning methods, together with the reinforcement learning (RL) methodology, which permits the autonomous agent to automatically learn its structure and its necessary action in each position in the environment. The improved version of the RL-HNFP model implements a better defuzzification method, improving the agent's behaviour. The extended RL-HNFP model was evaluated in a multi-obstacle environment, providing good performance and demonstrating the agent's autonomy.

This work presents the development of a methodology based on Monte Carlo simulation, fuzzy numbers and in the real options theory to determine the real options value under technical and market uncertainties. The objective of the proposed methodology is to substantially reduce the computational time involved, facilitating the decision taking process. The methodology involves: fuzzy numbers, to represent certain types of uncertainties that does not have a known stochastic process that can correctly model them; stochastic processes to represent other uncertainties; and Monte Carlo simulation to generate a good approximation of the real option value. This methodology was evaluated in problems of expansion option in the area of oil exploration and production, attaining the same results provided by conventional techniques but with a significant reduction in the necessary computational time.

Great achievements in the area of evolvable hardware have been reported in the last decade. Evolvable hardware comprises an evolutionary algorithm, such as a genetic algorithm, and a reconfigurable hardware, such as field programmable analog arrays and field programmable gate arrays. In these devices, internal circuits' connections can be programmed for specific applications. In this paper, configurability and evolvability issues are addressed in the domain of synthesis of analog circuits on an analog reconfigurable platform. First, implementation aspects and characteristics are presented: then some circuits' syntheses are discussed. Finally the authors present their view on how the techniques and approaches studied in the domain of the synthesis of analog circuits could be extended and applied in other domains, such as molecular electronics.

This work presents a new hybrid neuro-fuzzy model for automatic learning of actions taken by agents. The main objective of this new model is to provide an agent with intelligence, making it capable, by interacting with its environment, to acquire and retain knowledge for reasoning (infer an action). This new model, named reinforcement learning hierarchical neuro-fuzzy politree (RL-HNFP), descends from the reinforcement learing hierarchical neuro-fuzzy BSP (RL-HNFB) that uses binary space partitioning. By using hierarchical partitioning methods, together with the reinforcement learning (RL) methodology, a new class of neuro-fuzzy systems (SNF) was obtained, which executes, in addition to automatically learning its structure, the autonomous learning of the actions to be taken by an agent. These characteristics represent an important differential when compared with the existing intelligent agents learning systems. The obtained results demonstrate the potential of this new model, which operates without any prior information, such as number of rules, rules specification, or number of partitions that the input space should have.

This work discusses an Evolvable Hardware (EHW) platform for the synthesis of analog electronic circuits. The EHW analog platform, named PAMA (Programmable Analog Multiplexer Array), is a reconfigurable platform that consists of integrated circuits whose internal connections can be programmed by Evolutionary Computation techniques, such as Genetic Algorithms, to synthesize circuits. The PAMA is classified as Field Programmable Analog Array (FPAA). FPAAs have just recently appeared, and most projects are being carried out in universities and research centers. They constitute the state of the art in the technology of reconfigurable platforms. These devices will become the building blocks of a forthcoming class of hardware, with the important features of self-adaptation and self-repairing, through automatic reconfiguration. The PAMA platform architectural details, concepts and characteristics are discussed. Three case studies, with promising results, are described: an operational amplifier, a logarithmic amplifier and a membership function circuit of a fuzzy logic controller.

This paper reviews the multiobjective fitness evaluation method called energy minimization (Zebulum et al., 1998; 2000), and presents an analysis of the method's behavior when used in a genetic algorithm applied to production scheduling of a petroleum refinery. The experimental results are presented and analyzed, leading to an overall evaluation of the benefits provided by the model.

This work investigates the application of variable length representation (VLR) evolutionary algorithms (EAs) in the field of Evolutionary Electronics. We propose a number of VLR methodologies that can cope with the main issues of variable length evolutionary systems. These issues include the search for efficient ways of sampling a genome space with varying dimensionalities, the task of balancing accuracy and parsimony of the solutions, and the manipulation of non-coding segments. We compare the performance of three proposed VLR approaches to sample the genome space: Increasing Length Genotypes, Oscillating Length Genotypes, and Uniformly Distributed Initial Population strategies. The advantages of reusing genetic material to replace non-coding segments are also emphasized in this work. It is shown, through examples in both analog and digital electronics, that the variable length genotype's representation is natural to this particular domain of application. A brief discussion on biological genome evolution is also provided.