Evolution Strategies in Optimization Problems

Evolution Strategies are inspired in biology and part of a larger research field known as Evolutionary Algorithms. Those strategies perform a random search in the space of admissible functions, aiming to optimize some given objective function. We show that simple evolution strategies are a useful tool in optimal control, permitting to obtain, in an efficient way, good approximations to the solutions of some recent and challenging optimal control problems.Comment: Partially presented at the 5th Junior European Meeting on "Control and Information Technology" (JEM'06), Sept 20-22, 2006, Tallinn, Estonia. To appear in "Proceedings of the Estonian Academy of Sciences -- Physics Mathematics

FairGBM: Gradient Boosting with Fairness Constraints

Machine Learning (ML) algorithms based on gradient boosted decision trees (GBDT) are still favored on many tabular data tasks across various mission critical applications, from healthcare to finance. However, GBDT algorithms are not free of the risk of bias and discriminatory decision-making. Despite GBDT's popularity and the rapid pace of research in fair ML, existing in-processing fair ML methods are either inapplicable to GBDT, incur in significant train time overhead, or are inadequate for problems with high class imbalance. We present FairGBM, a learning framework for training GBDT under fairness constraints with little to no impact on predictive performance when compared to unconstrained LightGBM. Since common fairness metrics are non-differentiable, we employ a "proxy-Lagrangian" formulation using smooth convex error rate proxies to enable gradient-based optimization. Additionally, our open-source implementation shows an order of magnitude speedup in training time when compared with related work, a pivotal aspect to foster the widespread adoption of FairGBM by real-world practitioners

Promoting Fairness through Hyperparameter Optimization

Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric- or model-specific, require access to sensitive attributes at inference time, or carry high development or deployment costs. This work explores the unfairness that emerges when optimizing ML models solely for predictive performance, and how to mitigate it with a simple and easily deployed intervention: fairness-aware hyperparameter optimization (HO). We propose and evaluate fairness-aware variants of three popular HO algorithms: Fair Random Search, Fair TPE, and Fairband. We validate our approach on a real-world bank account opening fraud case-study, as well as on three datasets from the fairness literature. Results show that, without extra training cost, it is feasible to find models with 111% mean fairness increase and just 6% decrease in performance when compared with fairness-blind HO.Comment: arXiv admin note: substantial text overlap with arXiv:2010.0366

Estudo da durabilidade do efeito de termoregulação em malhas com materiais de mudança de fase

O objectivo do presente trabalho foi estudar o efeito das lavagens domésticas em substratos acabados com um acabamento de termoregulação à base de materiais de mudança de fase microencapsulados (PCM´s). Neste estudo utilizou-se uma malha Jersey tingida com corante reactivo com média substantividade e reactividade. A análise da influência das lavagens foi estudada com base nas propriedades térmicas e na análise da dispersão de microcápsulas através de microscopia electrónica. Os resultados obtidos mostram que as lavagens fazem com que se verifique uma diminuição do número de microcápsulas no substrato, influenciando o desempenho de termoregulação. Este efeito é particularmente visível a partir da 15ªlavagem

Disentangling the cost efficiency of jointly provided water and wastewater services

Providing operators with objective incentives for cost efficiency and continuous improvement in the provision of public services are major concerns for regulators. Measuring efficiency empirically is complex and this complexity is accentuated when the same operator is responsible for delivering more than one service (e.g. in order to explore potential economies of scope). Based on a sample of operators that provide water and wastewater services, this paper uses a shared input data envelopment analysis model to measure separately the efficiency of each service. The results show that a single measure may not provide enough information for monitoring multi-utilities. Together with other indicators, the proposed model can assist decision-makers in prioritizing efforts to improve overall efficiency

Economic cost recovery in the recycling of packaging waste: the case of Portugal

The recycling of packaging waste is an objective of the Community with clear targets set in the European law. The study of the institutional arrangements, recycling systems and of the costs that resulted from this environmental policy represents an ongoing effort. While each member state has currently its own packaging waste management system, there is still a lack of evidence regarding the actual costs of recycling and on how these costs have been distributed among stakeholders. This paper addresses the Portuguese framework and discusses the financial transfers undertaken by the entity that manages the Green Dot scheme. For this purpose, we use data from the entities in charge of selective collection and sorting of household packaging waste for the year 2010. We compare the financial transfers of the Green Dot company with the costs incurred by the local authorities (which are generally in charge of selective collection and sorting) and open a discussion on the extent to which the principles of the Directive on Packaging and Packaging Waste are being fulfilled in practice. Currently, the Green Dot company is only bearing 77% of the financial costs of the recycling systems in operation in Portugal. The unit cost of the selective collection and sorting of packaging waste is estimated to be 204 €/ton collected

Costs and benefits of packaging waste recycling systems

This Special Issue provides several different perspectives on the complex issue of packaging waste recycling. It comprises a diverse and rich set of contributions with insights from very different disciplines that range from economics to engineering. All types of “costs and benefits” are addressed in this collection of articles. In addition to the economic and strictly financial impacts of selective collection and sorting of packaging waste, several authors discuss other types of impacts, such as the environmental and social ones. The reader will find articles that address recycling systems as a whole, pieces that focus on specific impacts and detailed discussions of particular material streams or waste management strategies. The Special Issue represents an indispensable resource for academics, policy-makers and practitioners with interests in recycling and packaging waste management