338 research outputs found
Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures
The protein-folding problem has been extensively studied during the last
fifty years. The understanding of the dynamics of global shape of a protein and the influence
on its biological function can help us to discover new and more effective
drugs to deal with diseases of pharmacological relevance. Different computational approaches
have been developed by different researchers in order to foresee the threedimensional
arrangement of atoms of proteins from their sequences. However, the
computational complexity of this problem makes mandatory the search for new models,
novel algorithmic strategies and hardware platforms that provide solutions in a
reasonable time frame. We present in this revision work the past and last tendencies
regarding protein folding simulations from both perspectives; hardware and software.
Of particular interest to us are both the use of inexact solutions to this computationally hard problem as
well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y TecnologÃa, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.IngenierÃa, Industria y Construcció
An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification
Recurrent neural networks (RNNs) are powerful tools for learning information from
temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training
issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation
approach is proposed for training deep RNNs for the sentiment classification task. The approach
employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification
problems by considering only three individual solutions in each iteration. BA-3+ combines the
collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep
recurrent learning architecture. Local learning with exploitative search utilises the greedy selection
strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to
handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy
of SVD. Global learning with explorative search achieves faster convergence without getting trapped
at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning
architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and
asymmetric distribution of the datasets from different domains, including Twitter, product reviews,
and movie reviews. Comparative results have been obtained for advanced deep language models and
Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged
to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE,
and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have
improved at least with a 30–40% improvement than the standard SGD algorithm for all classification
datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the
RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance
of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks
(RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The
improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the
complex classification task, and it can handle the vanishing and exploding gradients problem of
deep RNNs
Advances in Evolutionary Algorithms
With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field
A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area
Evolutionary Computation in System Identification: Review and Recommendations
Two of the steps in system identification are model structure selection and parameter estimation. In model structure selection, several model structures are evaluated and selected. Because the evaluation of all possible model structures during selection and estimation of the parameters requires a lot of time, a rigorous method in which these tasks can be simplified is usually preferred. This paper reviews cumulatively some of the methods that have been tried since the past 40 years. Among the methods, evolutionary computation is known to be the most recent one and hereby being reviewed in more detail, including what advantages the method contains and how it is specifically implemented. At the end of the paper, some recommendations are provided on how evolutionary computation can be utilized in a more effective way. In short, these are by modifying the search strategy and simplifying the procedure based on problem a priori knowledge
Memetic micro-genetic algorithms for cancer data classification
Fast and precise medical diagnosis of human cancer is crucial for treatment decisions. Gene selection consists of identifying a set of informative genes from microarray data to allow high predictive accuracy in human cancer classification. This task is a combinatorial search problem, and optimisation methods can be applied for its resolution. In this paper, two memetic micro-genetic algorithms (MμV1 and MμV2) with different hybridisation approaches are proposed for feature selection of cancer microarray data. Seven gene expression datasets are used for experimentation. The comparison with stochastic state-of-the-art optimisation techniques concludes that problem-dependent local search methods combined with micro-genetic algorithms improve feature selection of cancer microarray data.Fil: Rojas, Matias Gabriel. Universidad Nacional de Lujan. Centro de Investigacion Docencia y Extension En Tecnologias de la Informacion y Las Comunicaciones.; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Mendoza; ArgentinaFil: Olivera, Ana Carolina. Universidad Nacional de Cuyo. Facultad de IngenierÃa; Argentina. Universidad Nacional de Lujan. Centro de Investigacion Docencia y Extension En Tecnologias de la Informacion y Las Comunicaciones.; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Mendoza; ArgentinaFil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Ciencias e IngenierÃa de la Computación; ArgentinaFil: Vidal, Pablo Javier. Universidad Nacional de Cuyo. Facultad de IngenierÃa; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Mendoza; Argentin
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