Multi-objective constrained optimization for energy applications via tree ensembles

Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e.g. economic gain vs. environmental impact. Moreover, a large number of input variables and different variable types, e.g. continuous and categorical, are challenges commonly present in real-world applications. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems with heterogeneous variable spaces for which underlying system dynamics are either too complex to model or unknown. In an extensive case study comprised of synthetic benchmarks and relevant energy applications we demonstrate the competitive performance and sampling efficiency of the proposed algorithm compared to other state-of-the-art tools, making it a useful all-in-one solution for real-world applications with limited evaluation budgets

High-Level Synthesis Hardware Design for FPGA-Based Accelerators: Models, Methodologies, and Frameworks

Hardware accelerators based on field programmable gate array (FPGA) and system on chip (SoC) devices have gained attention in recent years. One of the main reasons is that these devices contain reconfigurable logic, which makes them feasible for boosting the performance of applications. High-level synthesis (HLS) tools facilitate the creation of FPGA code from a high level of abstraction using different directives to obtain an optimized hardware design based on performance metrics. However, the complexity of the design space depends on different factors such as the number of directives used in the source code, the available resources in the device, and the clock frequency. Design space exploration (DSE) techniques comprise the evaluation of multiple implementations with different combinations of directives to obtain a design with a good compromise between different metrics. This paper presents a survey of models, methodologies, and frameworks proposed for metric estimation, FPGA-based DSE, and power consumption estimation on FPGA/SoC. The main features, limitations, and trade-offs of these approaches are described. We also present the integration of existing models and frameworks in diverse research areas and identify the different challenges to be addressed

Fairness-Aware Hyperparameter Optimization

In recent years, increased usage of machine learning algorithms has been accompanied by several reports of machine bias in areas from recidivism assessment, to job-applicant screening tools, and estimating mortgage default risk. Additionally, recent advances in machine learning have prominently featured so-called "black-box" models (e.g. neural networks), in which we can see its inputs and outputs, but with limited capability for inspecting its decision-making process. As a result, it is increasingly imperative to monitor and control fairness of developed models for detecting discrimination against sub-groups of the population (e.g. based on race, gender, or age). State-of-the-art machine learning algorithms require the definition of a large number of hyperparameters to govern how they learn and generalize to unseen data. Current hyperparameter search algorithms aim to tune these knobs in order to optimize for a global performance metric (e.g. accuracy). At the same time, fairness metrics are equally impacted by varying hyperparameter values, but there is comparatively little research on optimizing for multiple objectives. Consequently, we aim to study how to achieve efficient hyperparameter optimization for multi-objective goals, and corresponding trade-offs. We develop a hyperparameter optimization framework that supports the definition of secondary objectives or constraints, and experiment with multiple fairness metrics (e.g. equality of opportunity). Furthermore, we explore a fraud detection case study, and assess the framework's effectiveness in this context

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Adaptive Automated Machine Learning

The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open. In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions: 1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue. 2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures. 3. We introduce an online AutoML framework that allows the incremental adaptation of ML models. We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances