20,358 research outputs found
Evolutionary algorithm-based analysis of gravitational microlensing lightcurves
A new algorithm developed to perform autonomous fitting of gravitational
microlensing lightcurves is presented. The new algorithm is conceptually
simple, versatile and robust, and parallelises trivially; it combines features
of extant evolutionary algorithms with some novel ones, and fares well on the
problem of fitting binary-lens microlensing lightcurves, as well as on a number
of other difficult optimisation problems. Success rates in excess of 90% are
achieved when fitting synthetic though noisy binary-lens lightcurves, allowing
no more than 20 minutes per fit on a desktop computer; this success rate is
shown to compare very favourably with that of both a conventional (iterated
simplex) algorithm, and a more state-of-the-art, artificial neural
network-based approach. As such, this work provides proof of concept for the
use of an evolutionary algorithm as the basis for real-time, autonomous
modelling of microlensing events. Further work is required to investigate how
the algorithm will fare when faced with more complex and realistic microlensing
modelling problems; it is, however, argued here that the use of parallel
computing platforms, such as inexpensive graphics processing units, should
allow fitting times to be constrained to under an hour, even when dealing with
complicated microlensing models. In any event, it is hoped that this work might
stimulate some interest in evolutionary algorithms, and that the algorithm
described here might prove useful for solving microlensing and/or more general
model-fitting problems.Comment: 14 pages, 3 figures; accepted for publication in MNRA
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach
Efficient identification of people and objects, segmentation of regions of
interest and extraction of relevant data in images, texts, audios and videos
are evolving considerably in these past years, which deep learning methods,
combined with recent improvements in computational resources, contributed
greatly for this achievement. Although its outstanding potential, development
of efficient architectures and modules requires expert knowledge and amount of
resource time available. In this paper, we propose an evolutionary-based neural
architecture search approach for efficient discovery of convolutional models in
a dynamic search space, within only 24 GPU hours. With its efficient search
environment and phenotype representation, Gene Expression Programming is
adapted for network's cell generation. Despite having limited GPU resource time
and broad search space, our proposal achieved similar state-of-the-art to
manually-designed convolutional networks and also NAS-generated ones, even
beating similar constrained evolutionary-based NAS works. The best cells in
different runs achieved stable results, with a mean error of 2.82% in CIFAR-10
dataset (which the best model achieved an error of 2.67%) and 18.83% for
CIFAR-100 (best model with 18.16%). For ImageNet in the mobile setting, our
best model achieved top-1 and top-5 errors of 29.51% and 10.37%, respectively.
Although evolutionary-based NAS works were reported to require a considerable
amount of GPU time for architecture search, our approach obtained promising
results in little time, encouraging further experiments in evolutionary-based
NAS, for search and network representation improvements.Comment: Accepted for presentation at the IEEE Congress on Evolutionary
Computation (IEEE CEC) 202
Optimization of micropillar sequences for fluid flow sculpting
Inertial fluid flow deformation around pillars in a microchannel is a new
method for controlling fluid flow. Sequences of pillars have been shown to
produce a rich phase space with a wide variety of flow transformations.
Previous work has successfully demonstrated manual design of pillar sequences
to achieve desired transformations of the flow cross-section, with experimental
validation. However, such a method is not ideal for seeking out complex
sculpted shapes as the search space quickly becomes too large for efficient
manual discovery. We explore fast, automated optimization methods to solve this
problem. We formulate the inertial flow physics in microchannels with different
micropillar configurations as a set of state transition matrix operations.
These state transition matrices are constructed from experimentally validated
streamtraces. This facilitates modeling the effect of a sequence of
micropillars as nested matrix-matrix products, which have very efficient
numerical implementations. With this new forward model, arbitrary micropillar
sequences can be rapidly simulated with various inlet configurations, allowing
optimization routines quick access to a large search space. We integrate this
framework with the genetic algorithm and showcase its applicability by
designing micropillar sequences for various useful transformations. We
computationally discover micropillar sequences for complex transformations that
are substantially shorter than manually designed sequences. We also determine
sequences for novel transformations that were difficult to manually design.
Finally, we experimentally validate these computational designs by fabricating
devices and comparing predictions with the results from confocal microscopy
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification
Convolutional Neural Networks (CNNs) have demonstrated their superiority in
image classification, and evolutionary computation (EC) methods have recently
been surging to automatically design the architectures of CNNs to save the
tedious work of manually designing CNNs. In this paper, a new hybrid
differential evolution (DE) algorithm with a newly added crossover operator is
proposed to evolve the architectures of CNNs of any lengths, which is named
DECNN. There are three new ideas in the proposed DECNN method. Firstly, an
existing effective encoding scheme is refined to cater for variable-length CNN
architectures; Secondly, the new mutation and crossover operators are developed
for variable-length DE to optimise the hyperparameters of CNNs; Finally, the
new second crossover is introduced to evolve the depth of the CNN
architectures. The proposed algorithm is tested on six widely-used benchmark
datasets and the results are compared to 12 state-of-the-art methods, which
shows the proposed method is vigorously competitive to the state-of-the-art
algorithms. Furthermore, the proposed method is also compared with a method
using particle swarm optimisation with a similar encoding strategy named IPPSO,
and the proposed DECNN outperforms IPPSO in terms of the accuracy.Comment: Accepted by The Australasian Joint Conference on Artificial
Intelligence 201
Evaluation of Hashing Methods Performance on Binary Feature Descriptors
In this paper we evaluate performance of data-dependent hashing methods on
binary data. The goal is to find a hashing method that can effectively produce
lower dimensional binary representation of 512-bit FREAK descriptors. A
representative sample of recent unsupervised, semi-supervised and supervised
hashing methods was experimentally evaluated on large datasets of labelled
binary FREAK feature descriptors
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