Mengjie Zhang

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Cooperative coevolution decomposes a problem into subcomponents and employs evolutionary algorithms for solving them. Cooperative coevolution has been effective for evolving neural networks. Different problem decomposition methods in cooperative coevolution determine how a neural network is decomposed and encoded which affects its performance. The problem(More)
We describe an approach to the use of genetic progreimming for object detection problems in which the locations of small objects of multiple classes in lau-ge pictures must be found. The evolved programs use a feature set computed from a square input field leirge enough to contain each of objects of interest and are applied, in moving window fashion, over(More)
Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the(More)
This paper describes a new approach to the use of Gaussian distribution in genetic programming (GP) for multiclass object classification problems. Instead of using predefined multiple thresholds to form different regions in the program output space for different classes, this approach uses probabilities of different classes, derived from Gaussian(More)
The problem of domain generalization is to take knowledge acquired from a number of related domains, where training data is available, and to then successfully apply it to previously unseen domains. We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for cross-domain object recognition.(More)
In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition. Specifically, we design a new model called Deep ReconstructionClassification Network (DRCN), which jointly learns a shared encoding representation for two tasks: i) supervised classification of labeled source data, and ii)(More)
This paper describes a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on(More)
Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyperheuristics have been developed and shown to be effective, efficient, easy to implement, and reusable in different shop(More)