Robust optimization of algorithmic trading systems

GAs (Genetic Algorithms) and GP (Genetic Programming) are investigated for finding robust Technical Trading Strategies (TTSs). TTSs evolved with standard GA/GP techniques tend to suffer from over-fitting as the solutions evolved are very fragile to small disturbances in the data. The main objective of this thesis is to explore optimization techniques for GA/GP which produce robust TTSs that have a similar performance during both optimization and evaluation, and are also able to operate in all market conditions and withstand severe market shocks. In this thesis, two novel techniques that increase the robustness of TTSs and reduce over-fitting are described and compared to standard GA/GP optimization techniques and the traditional investment strategy Buy & Hold. The first technique employed is a robust multi-market optimization methodology using a GA. Robustness is incorporated via the environmental variables of the problem, i.e. variablity in the dataset is introduced by conducting the search for the optimum parameters over several market indices, in the hope of exposing the GA to differing market conditions. This technique shows an increase in the robustness of the solutions produced, with results also showing an improvement in terms of performance when compared to those offered by conducting the optimization over a single market. The second technique is a random sampling method we use to discover robust TTSs using GP. Variability is introduced in the dataset by randomly sampling segments and evaluating each individual on different random samples. This technique has shown promising results, substantially beating Buy & Hold. Overall, this thesis concludes that Evolutionary Computation techniques such as GA and GP combined with robust optimization methods are very suitable for developing trading systems, and that the systems developed using these techniques can be used to provide significant economic profits in all market conditions

A Situation-Aware Fear Learning (SAFEL) Model for Robots

This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is to allow robots to learn complex temporal patterns of sensed environmental stimuli and create a representation of these patterns. This memory can be later associated with a negative or positive “emotion”, analogous to fear and confidence. Experiments with a real robot demonstrated SAFEL’s success in generating contextual fear conditioning behaviour with predictive capabilities based on situational information

Collaborative Improvement of Smart Manufacturing using Privacy-Preserving Federated Learning

Nowadays, data sharing among different sources is is very challenging in the manufac- turing domain, mainly due to industry competition, complicated bureaucratic processes, and privacy and security concerns. Centralized Machine Learning (ML) poses an essential aspect in several industries, including smart manufacturing. However this approach may lead to several issues regarding security and performance. In response to these problems, Federated Learning (FL) was created. FL is an innova- tive and decentralized approach to ML, focused on collaboration and data privacy. In this approach, data is kept in each source where it is trained locally, and only model weights or gradients are shared to create a global model. Although several works have already been implemented towards this problem, there are still many unresolved issues concerning the application of FL frameworks in smart manufacturing scenarios. Among the several issues found in the analysed works it is important to emphasize the disregard facing industry 4.0 architectures, strategies and the unavailability to improve those frameworks further. This work aims to build a FL framework for smart manufacturing with specific con- cerns in privacy and applicability in industrial scenarios. The main focus of this frame- work is to facilitate a collaborative approach in the application of ML to manufacturing by enabling the knowledge sharing for this purpose and taking privacy as a special concern. In addition, the implementation and testing of privacy-preserving algorithms, while im- proving the framework for industrial scenarios are emphasized. A modular approach is chosen to create a framework adapted to various industrial cases by implementing several nodes that focus on specific aspects of data collection, data treatment, connection with the FL system, and ML model management. The results revealed a competitive model performance of the framework compared to the centralized approach while keeping data at each source, protecting its privacy. The implemented framework also proved to be compliant with the IEEE Std 3652.1-2020 standard guidelines, attaining the established requirement levels.Atualmente, a partilha de dados entre diferentes fontes é um grande desafio no domí- nio da manufatura, principalmente devido à concorrência da indústria, processos burocrá- ticos complicados e preocupações de privacidade e segurança. O Machine Learning (ML) impõe-se como um aspeto essencial em várias indústrias, incluindo a manufatura inteli- gente. Contudo, esta abordagem pode levantar várias questões relativamente à segurança e ao desempenho. Em resposta a estes problemas, foi criado o Federated Learning (FL). FL é uma aborda- gem inovadora e descentralizada de ML, centrada na colaboração e privacidade de dados. Nesta abordagem, os dados são mantidos em cada fonte, onde são treinados localmente, e apenas os pesos ou gradientes dos modelos são partilhados para criar um modelo global. Embora vários trabalhos já tenham sido implementados visando esta temática, ainda existem muitas questões por resolver relativas à aplicação de frameworks de FL em ce- nários de manufatura inteligente. Entre as várias questões encontradas na literatura analisada, é importante enfatizar a desconsideração pelas arquiteturas e estratégias da indústria 4.0 e a indisponibilidade para melhorar essas frameworks. Este trabalho visa construir uma framework de FL aplicada à manufatura inteligente com preocupações específicas no que toca a matérias de privacidade e aplicabilidade em cenários industriais. O principal objectivo desta framework é facilitar uma abordagem colaborativa na aplicação de ML ao fabrico, permitindo a partilha de conhecimentos para este fim e enfatizando a preocupação na privacidade dos utilizadores. Uma abordagem modular foi escolhida para criar uma framework adaptada a vários casos industriais atra- vés da implementação de vários nós que se concentram em aspetos específicos da recolha de dados, tratamento de dados, ligação com o sistema de FL e gestão do modelo de ML. Os resultados revelaram um desempenho competitivo do modelo em relação a uma abordagem centralizada, mantendo os dados em cada fonte e protegendo a sua privaci- dade. A framework implementada também provou estar em conformidade com a norma IEEE Std 3652.1-2020, atingindo os níveis de exigência estabelecidos

Three essays on political economy

This thesis comprises three papers on political economy. We study how politicians are selected during elections in the first two papers. In the first paper, we study the individual characteristics (such as education, job, and experience) that render some candidates more successful than others. In the second paper, we study how information about a candidate’s characteristics affects voter behavior through a field/online experiment. While in the third paper, we introduce a new dataset and a methodological approach to retrieve granular precinct-level electoral results