Nonlinear bioreactors are considered essential technology in chemical and biochemical industries. This paper presents a proposal of a robust model based fault diagnosis in a nonlinear bioreactor, formulated as the sol...
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Nonlinear bioreactors are considered essential technology in chemical and biochemical industries. This paper presents a proposal of a robust model based fault diagnosis in a nonlinear bioreactor, formulated as the solution of an inverse problem. The optimization problem is solved by using four different evolutionary strategies: Particle Swarm Optimization (PSO), Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization with Memory (PSO-M), with DE resulting the best according to the evaluated quantitative indicators. The results obtained with this approach indicate advantages in comparison to other methods of fault diagnosis (FDI) present in literature. (C) 2016 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Context: evolutionary algorithms typically require large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate. Objective: To solve search-based software engineering (SE) ...
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The calibration of a hydrological model is an important task for obtaining accurate runoff simulation results for a specific watershed. Several optimisation algorithms have been applied during the last years for the a...
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The calibration of a hydrological model is an important task for obtaining accurate runoff simulation results for a specific watershed. Several optimisation algorithms have been applied during the last years for the automatic calibration of conceptual rainfall-runoff (CRR) models. The aim of this study is to compare the effectiveness and the efficiency of three evolutionary algorithms, namely the Shuffled Complex Evolution (SCE), the Genetic algorithms (GA) and the evolutionary Annealing-Simplex (EAS), for the calibration of the Medbasin-D daily CRR model. An improved calibration approach of Medbasin-D is presented, including a batch-processing module which enables the implementation of coupled simulation-optimisation routines. The enhanced Medbasin calibration module is employed in a watershed of the island of Crete (Greece), using several objective functions in order to test the optimisation algorithms under different hydrological flow conditions. The results reveal that, in terms of effectiveness, SCE and EAS performed equally well, while GA provided slightly worse optimal solutions. However, GA was computationally more efficient than SCE and EAS. Despite the discrepancies among the optimisation runs, the simulated hydrographs had a very similar response for the optimal parameter sets obtained by the same calibration criteria, indicating that all tested optimisation methods produce equally successful results with Medbasin-D model. Additionally, the selected objective function seems to have a more decisive effect on the final simulation outcomes.
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of- Speech for sEntiment Compression), ...
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This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate norm...
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This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24 h period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviours, but can be extracted as mathematical formulae for the parameterization of computational models. (C) 2016 The Authors. Published by Elsevier Ireland Ltd.
With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector mach...
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With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classiffication, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classiffication problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.
evolutionary algorithms are one of the most popular forms of optimization algorithms. They are comparatively easy to use and were successfully employed for a wide variety of practical applications. However, frequently...
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evolutionary algorithms are one of the most popular forms of optimization algorithms. They are comparatively easy to use and were successfully employed for a wide variety of practical applications. However, frequently, it is necessary to execute them in parallel in order to reduce the runtime. There are a number of different approaches for the parallelization of evolutionary algorithms, and various hardware platforms can be used for the parallel execution. However, not every platform is equally suitable for any kind of parallelization of evolutionary algorithms. In addition, it also depends on properties of the concrete optimization problem to be solved and on the used evolutionary algorithm, which platform is best suited for the execution. The present work observes this in detail for two common forms of parallelization of evolutionary algorithms - the island model and the global parallelization - and for four widely used parallel computing platforms - multi-core CPUs, clusters, graphics cards, and grids. Based on empirical and analytical investigations, it is determined, under which circumstances an architecture is better suited for the execution of a parallel evolutionary algorithm than another (and vice versa). Guidelines are derived that support users of parallel evolutionary algorithms with the choice of an appropriate platform. Copyright (c) 2016 John Wiley & Sons, Ltd.
A review of evolutionary algorithms (EAs) with applications to antenna and propagation problems is presented. EAs have emerged as viable candidates for global optimization problems and have been attracting the attenti...
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A review of evolutionary algorithms (EAs) with applications to antenna and propagation problems is presented. EAs have emerged as viable candidates for global optimization problems and have been attracting the attention of the research community interested in solving real-world engineering problems, as evidenced by the fact that very large number of antenna design problems have been addressed in the literature in recent years by using EAs. In this paper, our primary focus is on Genetic algorithms (GAs), Particle Swarm Optimization (PSO), and Differential Evolution (DE), though we also briefly review other recently introduced nature-inspired algorithms. An overview of case examples optimized by each family of algorithms is included in the paper.
This paper proposes a first step towards multidisciplinary design of building spatial designs. Two criteria, total surface area (i.e. energy performance) and compliance (i.e. structural performance), are combined in a...
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ISBN:
(纸本)9783319458236;9783319458229
This paper proposes a first step towards multidisciplinary design of building spatial designs. Two criteria, total surface area (i.e. energy performance) and compliance (i.e. structural performance), are combined in a multicriteria optimisation framework. A new way of representing building spatial designs in a mixed integer parameter space is used within this framework. Two state-of-the-art algorithms, namely NSGA-II and SMS-EMOA, are used and compared to compute Pareto front approximations for problems of different size. Moreover, the paper discusses domain specific search operators, which are compared to generic operators, and techniques to handle constraints within the mutation. The results give first insights into the trade-off between energy and structural performance and the scalability of the approach.
This short paper contains an extended list of references to diversity preservation methodologies, classified following the taxonomy presented in a previous publication. The list has been updated according to the contr...
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ISBN:
(纸本)9781450343237
This short paper contains an extended list of references to diversity preservation methodologies, classified following the taxonomy presented in a previous publication. The list has been updated according to the contributions sent to the workshop "Measuring and Promoting Diversity in evolutionary Computation", held during the conference GECCO 2016.
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