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

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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

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

Produtos derivados de pescado provenientes da cachara.

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