A single-objective and a multi-objective genetic algorithm to generate accurate and interpretable fuzzy rule based classifiers for the analysis of complex financial data

Nowadays, organizations deal with rapidly increasing amount of data that is stored in their databases. It has therefore become of crucial importance for them to identify the necessary patterns in these large databases to turn row data into valuable and actionable information. By exploring these important datasets, the organizations gain competitive advantage against other competitors, based on the assumption that the added value of Knowledge Management Systems strength is first and foremost to facilitate the decision making process. Especially if we consider the importance of knowledge in the 21st century, data mining can be seen as a very effective tool to explore the essential data that foster competitive gain in a changing environment. The overall aim of this study is to design the rule base component of a fuzzy rule-based system (FRBS) through the use of genetic algorithms. The main objective is to generate accurate and interpretable models of the data trying to overcome the existing tradeoff between accuracy and interpretability. We propose two different approaches: an accuracy-driven single-objective genetic algorithm, and a three-objective genetic algorithm that produce a Pareto front approximation, composed of classifiers with different tradeoffs between accuracy and complexity. The proposed approaches have been compared with two other systems, namely a rule selection single-objective algorithm, and a three-objective algorithm. The latter has been developed by the University of Pisa and is able to generate the rule base, while simultaneously learning the definition points of the membership functions, by taking into account both the accuracy and the interpretability of the final model

High Performance Reconfigurable Fuzzy Logic Device for Medical Risk Evaluation

To date cardiovascular diseases (CVD) account for approximately 35% of all deaths worldwide. Many of these deaths are preventable if the risk of developing them can be accurately assessed early. Medical devices in use today cannot determine a patient's risk of developing a CVD condition. If accurate risk assessment was readily available to doctors, they can track rising trends in risk levels and recommend preventative measures for their patients. If patients had this risk assessment information before symptoms developed or life-threatening conditions occurred, they can contact their doctors to inquire about recommendations or seek help in emergency situations.This thesis research proposes the idea of using evolutionary programmed and tuned fuzzy logic controllers to diagnose a patient's risk of developing a CVD condition. The specific aim of this research seeks to advance the flexibility and functionality of fuzzy logic systems without sacrificing high speed and low resource utilization. The proposed system can be broken down into two layers. The bottom layer contains the controller that implements the fuzzy logic model and calculates the patient's risk of developing a CVD. The controller is designed in a context switchable hardware architecture the can be reconfigured to assess the risk of different CVD diseases. The top layer implements the evolutionary genetic algorithm in software, which configures the fuzzy parameters that optimize the behavior of the controller. The current implementation inputs patient's personal data such as electrocardiogram (ECG) wave features, age and body mass index (BMI) and outputs a risk percentage for Sinus Bradycardia (SB), a common cardiac arrhythmia. We validated this system via Matlab and Modelsim simulations and built the first prototype on a Xilinx Virtex-5 FPGA platform. Experimental results show that this 3-input-1-output fuzzy controller with 5 fuzzy sets per variable and 125 rule propositions produces results within an interval of approximately 1us while reducing hardware resource utilization by at least 25% when compared with existing designs

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks.

Política de acceso abierto tomada de: https://www.aimspress.com/index/news/solo-detail/openaccesspolicyThe blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict “buy” or “sell” decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.This research was funded by the Universitat de Barcelona, under the grant UB-AE-AS017634

An evolutionary approach to optimising neural network predictors for passive sonar target tracking

Object tracking is important in autonomous robotics, military applications, financial time-series forecasting, and mobile systems. In order to correctly track through clutter, algorithms which predict the next value in a time series are essential. The competence of standard machine learning techniques to create bearing prediction estimates was examined. The results show that the classification based algorithms produce more accurate estimates than the state-of-the-art statistical models. Artificial Neural Networks (ANNs) and K-Nearest Neighbour were used, demonstrating that this technique is not specific to a single classifier. [Continues.

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field