Algoritmo genético para construção de ensembles de redes neurais: aplicação à língua eletrônica.

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Many-objectives optimization: a machine learning approach for reducing the number of objectives

Solving real-world multi-objective optimization problems using Multi-Objective Optimization Algorithms becomes difficult when the number of objectives is high since the types of algorithms generally used to solve these problems are based on the concept of non-dominance, which ceases to work as the number of objectives grows. This problem is known as the curse of dimensionality. Simultaneously, the existence of many objectives, a characteristic of practical optimization problems, makes choosing a solution to the problem very difficult. Different approaches are being used in the literature to reduce the number of objectives required for optimization. This work aims to propose a machine learning methodology, designated by FS-OPA, to tackle this problem. The proposed methodology was assessed using DTLZ benchmarks problems suggested in the literature and compared with similar algorithms, showing a good performance. In the end, the methodology was applied to a difficult real problem in polymer processing, showing its effectiveness. The algorithm proposed has some advantages when compared with a similar algorithm in the literature based on machine learning (NL-MVU-PCA), namely, the possibility for establishing variable–variable and objective–variable relations (not only objective–objective), and the elimination of the need to define/chose a kernel neither to optimize algorithm parameters. The collaboration with the DM(s) allows for the obtainment of explainable solutions.This research was funded by POR Norte under the PhD Grant PRT/BD/152192/2021. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT (Portuguese Foundation for Science and Technology) under the projects UIDB/05256/2020, and UIDP/05256/2020, the Center for Mathematical Sciences Applied to Industry (CeMEAI) and the support from the São Paulo Research Foundation (FAPESP grant No 2013/07375-0, the Center for Artificial Intelligence (C4AI-USP), the support from the São Paulo Research Foundation (FAPESP grant No 2019/07665-4) and the IBM Corporation

Use of data analysis techniques for multi-objective optimization of real problems: decision making

Most, if not all, real optimization problems can be seen as multi-objective since several objectives are to be satisfied concurrently and are often conflicting. Also, due to the high computation times usually required by the numerical modelling routines available to calculate the values of the objective function, as a function of the decision variables, it is necessary to develop alternative optimization methodologies able to reduce the number of solutions to be evaluated, i.e., if compared with the procedures typically employed, such as evolutionary algorithms. Moreover, in a multi-objective environment, it is also necessary at the end of the optimization process to select a single solution from the pool of optimal non-dominated solutions obtained. Real industrial processes can be characterized by different types of data that can influence assertively its performance. For example, in the industrial process studied here, polymer processing, variables related to operating conditions of the machine, polymer properties and system geometry affect its operation since the thermomechanical environment developed allows obtaining mathematical relationships between these design variables and the objectives to be accomplished. This enables the direct process optimization using those routines to evaluate the solutions proposed by the optimization algorithms. However, this routine must be run several times, implying high computation times due to the sophistication of the numerical codes This work aims to apply Artificial Intelligence based on a data analysis technique, designated by DAMICORE, to surpass those limitations, improve the optimization process and help the selection of the best-equilibrated solution at the end. An example from single screw polymer extrusion is used to illustrate the efficient use of a methodology proposed, with a focus on decision making. Solving Multi-Objective Optimization Problems (MOOP) requires some interaction with a DM, for example, an expert in the field. The aim is to use data analysis techniques to reduce and improve the quality of those interactions, which can be done by integrating optimization methodologies with data analysis tools, i.e., the use of data to drive the optimization. At least, two different possibilities can be applied by data-driven optimization: i) replacement of the original method of calculating the objectives by a metamodel or surrogate, and 2) helping the computer in deciding about the best solutions to the problem. The aim here is to use the DAMICORE framework to facilitate the optimization taking into account the limitations/characteristics referred to above. The DAMICORE structure is based on the estimation of distances by compression algorithms called Normalized Compression Distance (NCD). Then, a Feature Sensitivity Optimization based on Phylogram Analysis (FS-OPA) is used to find the set of principal features related to the real problem environment. The present study focus on two levels of learning, which will be used to study an industrial case study using real data: First-level learning – the aim is to find clusters of variables sharing information, designated by clades, each representing the set of variables with important interactions. The result of this level is a table with a list of variables with a cluster per row. Second-level learning – the application of FS-OPA allows the estimation of the contribution of each clade of variables to the objectives, which is made by determining the distance between the clades of objectives (oclade) to each variable clade (vclade) using the phylogram obtained. These distances are an estimation of the influence of a clade to improve an objective. The results of this level are two different matrices, one with the phylogram distances from vclades to oclades and the second with the relative phylograms distances from each variable to each objective. From a practical point of view, the application of this method to the data of each population of solutions previously obtained during the multi-objective optimization using evolutionary algorithms will allow capturing the interactions between the decision variables and the objectives and, in the end, select the most important objectives to the process. Therefore, the multi-dimensional space, that results from the six objectives existent in the problem solved, can be reduced, which will help the decision maker in selecting in an easy way the solution to be applied in real practice. The results obtained for this practical example are in agreement with the expected thermomechanical behaviour of the process, which demonstrated that AI techniques can be useful in solving practical engineering problems

Combining artificial neural networks and evolution to solve multiobjective knapsack problems

The multiobjective knapsack problem (MOKP) is a combinatorial problem that arises in various applications, including resource allocation, computer science and finance. Evolutionary multiobjective optimization algorithms (EMOAs) can be effective in solving MOKPs. Though, they often face difficulties due to the loss of solution diversity and poor scalability. To address those issues, our study [2] proposes to generate candidate solutions by artificial neural networks. This is intended to provide intelligence to the search. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional search operators base on a binary crossover. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs.(undefined

Application of artificial intelligence techniques in the optimization of single screw polymer extrusion

As with most real optimization problems, polymer processing technologies can be seen as multi-objective optimization problems. Due to the high computation times required by the numerical modelling routines usually available to calculate the values of the objective function, as a function of the decision variables, it is necessary to develop alternative optimization methodologies able to reduce the number of solutions to be evaluated, when compared with the technics normally employed, such as evolutionary algorithms. Therefore, in this work is proposed the use of artificial intelligence based on a data analysis technique designated by DAMICORE surpasses those limitations. An example from single screw polymer extrusion is used to illustrate the efficient use of a methodology proposed.This research was partially funded by NAWA-Narodowa Agencja Wymiany Akademickiej, under grant PPN/ULM/2020/1/00125 and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No 734205–H2020-MSCA-RISE-2016. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT (Portuguese Foundation for Science and Technology) under the projects UIDB/05256/2020, and UID-P/05256/2020, the Center for Mathematical Sciences Applied to Industry (CeMEAI) and the support from the São Paulo Research Foundation (FAPESP grant No 2013/07375-0, the Center for Artificial Intelligence (C4AI-USP), the support from the São Paulo Research Foundation (FAPESP grant No 2019/07665-4) and the IBM Corporation

Neuroevolution for solving multiobjective knapsack problems

The multiobjective knapsack problem (MOKP) is an important combinatorial problem that arises in various applications, including resource allocation, computer science and finance. When tackling this problem by evolutionary multiobjective optimization algorithms (EMOAs), it has been demonstrated that traditional recombination operators acting on binary solution representations are susceptible to a loss of diversity and poor scalability. To address those issues, we propose to use artificial neural networks for generating solutions by performing a binary classification of items using the information about their profits and weights. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The main contribution of this study resides in developing a solution encoding and genotype-phenotype mapping for EMOAs to solve MOKPs. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional variation operators based on binary crossovers. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs.Portuguese “Fundação para a Ciência e Tecnologia” under grant PEst-C/CTM/LA0025/2013 (Projecto Estratégico - LA 25 - 2013-2014 - Strategic Project - LA 25 - 2013-2014

Defumação a quentes de filés de surubim.

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Vida de prateleira do pintado resfriado e conservado em gelo obtido em pesca artesanal no Pantanal.

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Elaboração de patê de pacu obtido da pesca artesanal no Pantanal.

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Tecnologias para a agroindústria: processamento artesanal do pescado do Pantanal.

O pescado proveniente de peixes do Pantanal pode ser utilizado para o processamento tecnológico em escala artesanal, com poucas adaptações tecnológicas, como demonstradas nas formulações apresentadas. Para a comercialização dos produtos, além das características sensoriais, deve-se considerar a existência de mercado consumidor, de escala de produção, qualidade do produto em seus vários aspectos e responsabilidade social e ambiental.bitstream/CPAP/56242/1/CT73.pdfFormato eletrônic