Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization

Automated deep neural architecture generation has gained increasing attention. However, exiting studies either optimize important design choices, without taking advantage of modern strategies such as residual/dense connections, or they optimize residual/dense networks but reduce search space by eliminating fine-grained network setting choices. To address the aforementioned weaknesses, we propose a novel particle swarm optimization (PSO)-based deep architecture generation algorithm, to devise deep networks with residual connections, whilst performing a thorough search which optimizes important design choices. A PSO variant is proposed which incorporates a new encoding scheme and a new search mechanism guided by non-uniformly randomly selected neighboring and global promising solutions for the search of optimal architectures. Specifically, the proposed encoding scheme is able to describe convolutional neural network architecture configurations with residual connections. Evaluated using benchmark datasets, the proposed model outperforms existing state-of-the-art methods for architecture generation. Owing to the guidance of diverse non-uniformly selected neighboring promising solutions in combination with the swarm leader at fine-grained and global levels, the proposed model produces a rich assortment of residual architectures with great diversity. Our devised networks show better capabilities in tackling vanishing gradients with up to 4.34 improvement of mean accuracy in comparison with those of existing studies

Effective Image Clustering with Differential Evolution Technique

The paper presents a novel approach of clustering image datasets with differential evolution (DE) technique. The differential evolution is a parallel direct search population based optimization method. From our simulations it is found that DE is able to optimize the quality measures of clusters of image datasets. To claim the superiority of DE based clustering we have compared the outcomes of DE with the classical K-means and popular Particle Swarm Optimization (PSO) algorithms for the same datasets. The comparisons results reveal the suitability of DE for image clustering in all image datasets

Classification of Synthetic Aperture Radar Images using Particle Swarm Optimization Technique

In this thesis, SAR image classification problem is considered as an optimization problem various clustering techniques are addressed in literature for SAR image classification. This thesis focuses on an evolutionary based stochastic optimization technique that is Particle Swarm Optimization (PSO) technique for classification of SAR images. This technique composes of three main processes: firstly, selecting training samples for every region in the SAR image. Secondly, training these samples using PSO, and obtain cluster center of every region. Finally, the classification of SAR image with respect to cluster center is obtained. To show the effectiveness of this approach, classified SAR images are obtained and compared with other clustering techniques such as K-means algorithm and Fuzzy C-means algorithm (FCM). The performance of PSO is found to be superior than other techniques in terms of classification accuracy and computational complexity. The result is validated with various SAR images

Word-level Textual Adversarial Attacking as Combinatorial Optimization

Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change to the original input. Word-level attacking, which can be regarded as a combinatorial optimization problem, is a well-studied class of textual attack methods. However, existing word-level attack models are far from perfect, largely because unsuitable search space reduction methods and inefficient optimization algorithms are employed. In this paper, we propose a novel attack model, which incorporates the sememe-based word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately. We conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM and BERT on three benchmark datasets. Experimental results demonstrate that our model consistently achieves much higher attack success rates and crafts more high-quality adversarial examples as compared to baseline methods. Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training. All the code and data of this paper can be obtained on https://github.com/thunlp/SememePSO-Attack.Comment: Accepted at ACL 2020 as a long paper (a typo is corrected as compared with the official conference camera-ready version). 16 pages, 3 figure

Optimization of Association Rule Using Heuristic Approach

Apriori algorithm is used to create all possible association rules among the items in the database, on the behalf of Association Rule Mining and Apriori Algorithm. Here proposed a new algorithm based on the Ant Colony Optimization algorithm to improve the result of association rule mining. Ant Colony Optimization (ACO) is a meta-heuristic approach that inspired by the real behaviour of ant colonies. The association rules create by Apriori algorithm after that find the rules from weakest set based on threshold value that will used the Ant Colony algorithm to reduce the association rules and discover the better quality of rules than apriori. In this research work proposed method focuses on reducing the scans of datasetss by optimization and improving the quality of rules generated for ACO