22,418 research outputs found
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number of
features. When artificial neural networks (ANNs) are used for modeling these
tasks, the network architectures tend to be large. Neuroevolution is an
effective approach for optimizing ANNs; however, there are two bottlenecks that
make their application challenging in case of high-dimensional networks using
direct encoding. First, classic evolutionary algorithms tend not to scale well
for searching large parameter spaces; second, the network evaluation over a
large number of training instances is in general time-consuming. In this work,
we propose an approach called the Limited Evaluation Cooperative
Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize
high-dimensional ANNs.
The proposed method aims to optimize the pre-synaptic weights of each
post-synaptic neuron in different subpopulations using a Cooperative
Co-evolutionary Differential Evolution algorithm, and employs a limited
evaluation scheme where fitness evaluation is performed on a relatively small
number of training instances based on fitness inheritance. We test LECCDE on
three datasets with various sizes, and our results show that cooperative
co-evolution significantly improves the test error comparing to standard
Differential Evolution, while the limited evaluation scheme facilitates a
significant reduction in computing time
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Designing neural networks for object recognition requires considerable
architecture engineering. As a remedy, neuro-evolutionary network architecture
search, which automatically searches for optimal network architectures using
evolutionary algorithms, has recently become very popular. Although very
effective, evolutionary algorithms rely heavily on having a large population of
individuals (i.e., network architectures) and is therefore memory expensive. In
this work, we propose a Regularized Evolutionary Algorithm with low memory
footprint to evolve a dynamic image classifier. In details, we introduce novel
custom operators that regularize the evolutionary process of a micro-population
of 10 individuals. We conduct experiments on three different digits datasets
(MNIST, USPS, SVHN) and show that our evolutionary method obtains competitive
results with the current state-of-the-art
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