1,491 research outputs found
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks
Image classification is a difficult machine learning task, where
Convolutional Neural Networks (CNNs) have been applied for over 20 years in
order to solve the problem. In recent years, instead of the traditional way of
only connecting the current layer with its next layer, shortcut connections
have been proposed to connect the current layer with its forward layers apart
from its next layer, which has been proved to be able to facilitate the
training process of deep CNNs. However, there are various ways to build the
shortcut connections, it is hard to manually design the best shortcut
connections when solving a particular problem, especially given the design of
the network architecture is already very challenging.
In this paper, a hybrid evolutionary computation (EC) method is proposed to
\textit{automatically} evolve both the architecture of deep CNNs and the
shortcut connections. Three major contributions of this work are: Firstly, a
new encoding strategy is proposed to encode a CNN, where the architecture and
the shortcut connections are encoded separately; Secondly, a hybrid two-level
EC method, which combines particle swarm optimisation and genetic algorithms,
is developed to search for the optimal CNNs; Lastly, an adjustable learning
rate is introduced for the fitness evaluations, which provides a better
learning rate for the training process given a fixed number of epochs. The
proposed algorithm is evaluated on three widely used benchmark datasets of
image classification and compared with 12 peer Non-EC based competitors and one
EC based competitor. The experimental results demonstrate that the proposed
method outperforms all of the peer competitors in terms of classification
accuracy
Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges
A variety of methods have been applied to the architectural configuration and
learning or training of artificial deep neural networks (DNN). These methods
play a crucial role in the success or failure of the DNN for most problems and
applications. Evolutionary Algorithms (EAs) are gaining momentum as a
computationally feasible method for the automated optimisation and training of
DNNs. Neuroevolution is a term which describes these processes of automated
configuration and training of DNNs using EAs. While many works exist in the
literature, no comprehensive surveys currently exist focusing exclusively on
the strengths and limitations of using neuroevolution approaches in DNNs.
Prolonged absence of such surveys can lead to a disjointed and fragmented field
preventing DNNs researchers potentially adopting neuroevolutionary methods in
their own research, resulting in lost opportunities for improving performance
and wider application within real-world deep learning problems. This paper
presents a comprehensive survey, discussion and evaluation of the
state-of-the-art works on using EAs for architectural configuration and
training of DNNs. Based on this survey, the paper highlights the most pertinent
current issues and challenges in neuroevolution and identifies multiple
promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
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