43,416 research outputs found
Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization
Despite their claimed biological plausibility, most self organizing networks
have strict topological constraints and consequently they cannot take into
account a wide range of external stimuli. Furthermore their evolution is
conditioned by deterministic laws which often are not correlated with the
structural parameters and the global status of the network, as it should happen
in a real biological system. In nature the environmental inputs are noise
affected and fuzzy. Which thing sets the problem to investigate the possibility
of emergent behaviour in a not strictly constrained net and subjected to
different inputs. It is here presented a new model of Evolutionary Neural Gas
(ENG) with any topological constraints, trained by probabilistic laws depending
on the local distortion errors and the network dimension. The network is
considered as a population of nodes that coexist in an ecosystem sharing local
and global resources. Those particular features allow the network to quickly
adapt to the environment, according to its dimensions. The ENG model analysis
shows that the net evolves as a scale-free graph, and justifies in a deeply
physical sense- the term gas here used.Comment: 16 pages, 8 figure
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Recently, increasing works have proposed to drive evolutionary algorithms
using machine learning models. Usually, the performance of such model based
evolutionary algorithms is highly dependent on the training qualities of the
adopted models. Since it usually requires a certain amount of data (i.e. the
candidate solutions generated by the algorithms) for model training, the
performance deteriorates rapidly with the increase of the problem scales, due
to the curse of dimensionality. To address this issue, we propose a
multi-objective evolutionary algorithm driven by the generative adversarial
networks (GANs). At each generation of the proposed algorithm, the parent
solutions are first classified into real and fake samples to train the GANs;
then the offspring solutions are sampled by the trained GANs. Thanks to the
powerful generative ability of the GANs, our proposed algorithm is capable of
generating promising offspring solutions in high-dimensional decision space
with limited training data. The proposed algorithm is tested on 10 benchmark
problems with up to 200 decision variables. Experimental results on these test
problems demonstrate the effectiveness of the proposed algorithm
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Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing
This work presents a new algorithm called evolutionary exploration of
augmenting convolutional topologies (EXACT), which is capable of evolving the
structure of convolutional neural networks (CNNs). EXACT is in part modeled
after the neuroevolution of augmenting topologies (NEAT) algorithm, with
notable exceptions to allow it to scale to large scale distributed computing
environments and evolve networks with convolutional filters. In addition to
multithreaded and MPI versions, EXACT has been implemented as part of a BOINC
volunteer computing project, allowing large scale evolution. During a period of
two months, over 4,500 volunteered computers on the Citizen Science Grid
trained over 120,000 CNNs and evolved networks reaching 98.32% test data
accuracy on the MNIST handwritten digits dataset. These results are even
stronger as the backpropagation strategy used to train the CNNs was fairly
rudimentary (ReLU units, L2 regularization and Nesterov momentum) and these
were initial test runs done without refinement of the backpropagation
hyperparameters. Further, the EXACT evolutionary strategy is independent of the
method used to train the CNNs, so they could be further improved by advanced
techniques like elastic distortions, pretraining and dropout. The evolved
networks are also quite interesting, showing "organic" structures and
significant differences from standard human designed architectures.Comment: 17 pages, 13 figures. Submitted to the 2017 Genetic and Evolutionary
Computation Conference (GECCO 2017
Neural network-based colonoscopic diagnosis using on-line learning and differential evolution
In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line evolution process that applies a differential evolution (DE) strategy to (re-) adapt the neural network to modified environmental conditions. Our approach looks at on-line training from the perspective of tracking the changing location of an approximate solution of a pattern-based, and thus, dynamically changing, error function. The proposed hybrid strategy is compared with other standard training methods that have traditionally been used for training neural networks off-line. Results in interpreting colonoscopy images and frames of video sequences are promising and suggest that networks trained with this strategy detect malignant regions of interest with accuracy
Global adaptation in networks of selfish components: emergent associative memory at the system scale
In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning
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
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