Using Artificial Ant Colonies for Automated Ant Colonies


Using Artificial Ant Colonies for Automated Ant Colonies – We present the first multi-agent system for the management of artificial ants. Our system is based on the artificial ant population management strategy where a colony is given a fixed number of ants given an initial number of ants. The ants are given the chosen number of ants according to the population size. Each ants is used to acquire resources based on their own population size. Therefore, a colony using the population size is asked to select a subset of the ants that are more important. The agent is then able to control such population by using different types of ant population and ants. This is done by implementing a reinforcement learning algorithm. On the web, we have released the first published experiments on different ant population management policies in a multiagent system.

Automatic segmentation into low-dimensional vectors has been proposed for a wide range of applications. Several algorithms have been proposed to perform this task but are still largely under-evaluated. In this paper, a novel class of adaptive automatic segmentation algorithms is proposed to address the challenging problem of the segmentation of low-dimensional representations by leveraging information about the features extracted from the image. To improve the segmentation accuracy, we employ a method of clustering data and a model of the embedding structure as inputs with a fixed feature space. A novel hierarchical clustering algorithm is proposed in order to alleviate the computational burden. The proposed hierarchical clustering algorithm combines the feature spaces into a shared space for the segmentation problem and achieves a compact segmentation function. The proposed multi-score hierarchical clustering algorithm can be applied to two types of datasets and achieves state-of-the-art results on different datasets.

3D Scanning Network for Segmentation of Medical Images

Learning a Visual Representation of a User’s Personal Information for Advertisment

Using Artificial Ant Colonies for Automated Ant Colonies

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  • The Evolution-Based Loss Functions for Deep Neural Network Training

    Deep learning for segmenting and ranking of large imagesAutomatic segmentation into low-dimensional vectors has been proposed for a wide range of applications. Several algorithms have been proposed to perform this task but are still largely under-evaluated. In this paper, a novel class of adaptive automatic segmentation algorithms is proposed to address the challenging problem of the segmentation of low-dimensional representations by leveraging information about the features extracted from the image. To improve the segmentation accuracy, we employ a method of clustering data and a model of the embedding structure as inputs with a fixed feature space. A novel hierarchical clustering algorithm is proposed in order to alleviate the computational burden. The proposed hierarchical clustering algorithm combines the feature spaces into a shared space for the segmentation problem and achieves a compact segmentation function. The proposed multi-score hierarchical clustering algorithm can be applied to two types of datasets and achieves state-of-the-art results on different datasets.


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