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Domain Generalization for Object Recognition with Multi-task Autoencoders

This work proposes a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for cross-domain object recognition and evaluates the performance of the algorithm on benchmark image recognition datasets, where the task is to learn features from multiple datasets and to then predict the image label from unseen datasets.
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A Survey on Evolutionary Computation Approaches to Feature Selection

This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms.
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Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

A new model called Deep Reconstruction-Classification Network (DRCN), which jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and unsupervised reconstruction of unlabeled target data, is designed.
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Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach

The experimental results show that the two PSO-based multi-objective algorithms can automatically evolve a set of nondominated solutions and the first algorithm outperforms the two conventional methods, the single objective method, and the two-stage algorithm.
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Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization

This paper proposes Scatter Component Analyis (SCA), a fast representation learning algorithm that can be applied to both domain adaptation and domain generalization and shows that scatter can be used to establish a theoretical generalization bound in the case of domain adaptation.
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Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms

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Evolving Deep Convolutional Neural Networks for Image Classification

A new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems and a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource.
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Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification

This article proposes an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks and shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.
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Domain Adaptive Neural Networks for Object Recognition

This work represents the first study of MMD measure in the context of neural networks and demonstrates that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set.
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Automated Design of Production Scheduling Heuristics: A Review

The state-of-the-art approaches are summarized, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling are summarized and suggested.
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