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

Evaluation of a Distributed Rendering Solution in the Computer Room of Universidad Icesi for the Industrial Design and Interactive Media Design Students

En este artículo se presentan los resultados de una evaluación realizada en la Universidad Icesi para encontrar alternativas de solución al problema de cuellos de botella y largos tiempos de renderizado que sufren los estudiantes de los programas de Diseño al trabajar con paquetes de diseño en sus cursos. En particular, se evalúan alternativas comerciales de administradores de granjas de renderizado, y se desarrolla una prueba de concepto de un administrador de granja de renderizado basado en paquetes open source de computación oportunista.This article presents the results of an evaluation made in the Univer­sidad Icesi to find solutions to the bottlenecks and long rendering times affecting the Design students while working with commercial design packages during their courses. In particular, commercial render farm managers are evaluated, and a proof-of-concept render farm manag­er based in open source opportunistic computation software is developed