GAMA:genetic automated machine learning assistant

GAMA is an AutoML package for end-users and AutoML researchers. It uses genetic programming to efficiently generate optimized machine learning pipelines given specific input data and resource constraints. A machine learning pipeline contains data preprocessing as well as a machine learning algorithm, with fine-tuned hyperparameter settings

A scalable and automated machine learning framework to support risk management

Due to the growth of data and wide spread usage of Machine Learning (ML) by non-experts, automation and scalability are becoming key issues for ML. This paper presents an automated and scalable framework for ML that requires minimum human input. We designed the framework for the domain of telecommunications risk management. This domain often requires non-ML-experts to continuously update supervised learning models that are trained on huge amounts of data. Thus, the framework uses Automated Machine Learning (AutoML), to select and tune the ML models, and distributed ML, to deal with Big Data. The modules included in the framework are task detection (to detect classification or regression), data preprocessing, feature selection, model training, and deployment. In this paper, we focus the experiments on the model training module. We first analyze the capabilities of eight AutoML tools: Auto-Gluon, Auto-Keras, Auto-Sklearn, Auto-Weka, H2O AutoML, Rminer, TPOT, and TransmogrifAI. Then, to select the tool for model training, we performed a benchmark with the only two tools that address a distributed ML (H2O AutoML and TransmogrifAI). The experiments used three real-world datasets from the telecommunications domain (churn, event forecasting, and fraud detection), as provided by an analytics company. The experiments allowed us to measure the computational effort and predictive capability of the AutoML tools. Both tools obtained high- quality results and did not present substantial predictive differences. Nevertheless, H2O AutoML was selected by the analytics company for the model training module, since it was considered a more mature technology that presented a more interesting set of features (e.g., integration with more platforms). After choosing H2O AutoML for the ML training, we selected the technologies for the remaining components of the architecture (e.g., data preprocessing and web interface).This work was executed under the project IRMDA - Intelligent Risk Management for the Digital Age, Individual Project, NUP: POCI-01-0247-FEDER-038526, co- funded by the Incentive System for Research and Technological Development, from the Thematic Operational Program Competitiveness of the national framework program - Portugal2020

A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost

This paper presents a benchmark of supervised Automated Machine Learning (AutoML) tools. Firstly, we an- alyze the characteristics of eight recent open-source AutoML tools (Auto-Keras, Auto-PyTorch, Auto-Sklearn, AutoGluon, H2O AutoML, rminer, TPOT and TransmogrifAI) and describe twelve popular OpenML datasets that were used in the benchmark (divided into regression, binary and multi-class classification tasks). Then, we perform a comparison study with hundreds of computational experiments based on three scenarios: General Machine Learning (GML), Deep Learning (DL) and XGBoost (XGB). To select the best tool, we used a lexicographic approach, considering first the average prediction score for each task and then the computational effort. The best predictive results were achieved for GML, which were further compared with the best OpenML public results. Overall, the best GML AutoML tools obtained competitive results, outperforming the best OpenML models in five datasets. These results confirm the potential of the general-purpose AutoML tools to fully automate the Machine Learning (ML) algorithm selection and tuning.Opti-Edge: 5G Digital Services Optimization at the Edge, Individual Project, NUP: POCI-01-0247-FEDER-045220, co-funded by the Incentive System for Research and Technological Development, from the Thematic Operational Program Competitiveness of the national framework program - Portugal202

Applications, Operational Architectures and Development of Virtual Power Plants as a Strategy to Facilitate the Integration of Distributed Energy Resources

In this article, we focus on the development and scope of virtual power plants (VPPs) as a strategy to facilitate the integration of distributed energy resources (DERs) in the power system. Firstly, the concepts about VPPs and their scope and limitations are introduced. Secondly, smart management systems for the integration of DERs are considered and a scheme of DER management through a bottom-up strategy is proposed. Then, we analyze the coordination of VPPs with the system operators and their commercial integration in the electricity markets. Finally, the challenges that must be overcome to achieve the large-scale implementation of VPPs in the power system are identified and discussed.The authors acknowledge the support from GISEL research group IT1191-19, as well as from the University of the Basque Country UPV/EHU (research group funding 181/18)

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

Development of a fuel-saving algorithm for a vehicle's driver assistant system

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2018.A fim de reduzir o consumo de combustível em sistemas de propulsão automotivos, a implementação de conjuntos motrizes híbridos, o downsizing de motores à combustão interna e a automatização do câmbio têm crescido no mercado de veículos de passeio. No entanto, as melhorias individuais em sistemas de um veículo não necessariamente aproximam a sua operação do ponto de ótima eficiência, e a adição de diferentes fontes de energia deve ser feita de forma metódica e estruturada, a fim de proporcionar ganhos consideráveis em consumo de combustível. Ademais, o comportamento do condutor e as trajetórias percorridas pelo veículo são características extremamente dependentes da região em análise, dificultando ainda mais o desenvolvimento de uma estratégia única de redução de consumo de combustível. Assim, a partir de um modelo de dinâmica longitudinal com três graus de liberdade para um veículo genérico, desenvolvido utilizando as equações de Euler-Lagrange do segundo tipo, essa dissertação tem como objetivo principal a proposta de um algoritmo para um assistente de direção automotivo, o qual promove a redução do consumo de combustível a partir do ajuste da relação de transmissão e abertura da válvula borboleta, em função da demanda de torque imposta pelo condutor, dinâmica do powertrain e características da fonte de potência. As características de desempenho do motor foram modeladas utilizando Redes Neurais Artificiais do tipo Feedforward Multi-Layer Perceptron, viabilizando a simulação de ciclos urbanos em tempo hábil e a inserção de propriedades relacionadas ao gradiente dos mapas estáticos no algoritmo do assistente de direção. O sistema foi implementado e simulado em Matlab , e seu desempenho avaliado através de um estudo de caso, utilizando modelos da literatura como referência.Abstract : The adoption of hybrid powertrain systems in passenger vehicles, as well as downsized engines and automatic transmissions, has been increasing in the last years as solutions to reduce the fuel consumption. However, the individual optimization of components or layout does not necessarily approximates the operation to conditions of maximum efficiency, and the addition of power sources should be done methodically, such that improvements of fuel efficiency can actually be achieved. Furthermore, the behavior of the driver and traffic conditions, factors which have major influence on the fuel consumption, vary with the geographic region, increasing the difficulty to develop a single solution to minimize the fuel consumption. Given such complex scenario, this dissertation proposes an algorithm for a Fuel-saving Driver Assistant System, which actuates on the throttle valve and gearbox, based on the demand of torque imposed by the driver, powertrain dynamics and characteristics of the power sources. In order to do so, a mathematical model of powertrain and longitudinal dynamics with 3 Degrees of Freedom was developed, which allows the simulation of urban traffic conditions. The performance of the engine was modeled using Artificial Neural Networks (ANN), which allies a flexible representation of the nonlinear characteristics of the power source, low computational costs and possibility to derive gradient information from the static maps, which is used by the Driver Assistant Algorithm. The system was implemented on Matlab and its performance compared to different models available in the literature