3,120 research outputs found
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
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
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Evolutionary neural architecture search for deep learning
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains.
However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters.
DNNs are often not used to their full potential because it is difficult to determine what architectures and hyperparameters should be used.
While several approaches have been proposed, computational complexity of searching large design spaces makes them impractical for large modern DNNs.
This dissertation introduces an efficient evolutionary algorithm (EA) for simultaneous optimization of DNN architecture and hyperparameters.
It builds upon extensive past research of evolutionary optimization of neural network structure.
Various improvements to the core algorithm are introduced, including:
(1) discovering DNN architectures of arbitrary complexity;
(1) generating modular, repetitive modules commonly seen in state-of-the-art DNNs;
(3) extending to the multitask learning and multiobjective optimization domains;
(4) maximizing performance and reducing wasted computation through asynchronous evaluations.
Experimental results in image classification, image captioning, and multialphabet character recognition show that the approach is able to evolve networks that are competitive with or even exceed hand-designed networks.
Thus, the method enables an automated and streamlined process to optimize DNN architectures for a given problem and can be widely applied to solve harder tasks.Computer Science
Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments
Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.This research is partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917
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