Hyperparameters optimization on neural networks for bond trading

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementArtificial Neural Networks have been recently spotlighted as de facto tools used for classification. Their ability to deal with complex decision boundaries makes them potentially suitable to work on trading within financial markets, namely on Bonds. Such classifier faces high flexibility on its parameters in parallel with great modularity of its techniques, arising thus the need to efficiently optimize its hyperparameters. To determine the most effcient search method to optimize almost the majority of the Neural Networks hyperparameters, we have compared the results obtained by the manual, evolutionary (genetic algorithm) and random search methods. The search methods compete on several metrics from which we aim to estimate the generalization capability, i.e. the capacity to correctly predict on unseen data. We have found the manual method to present better generalization results than the remaining automatic methods. Also, no benefit was found on the direction provided by the genetic search method when compared to the purely random. Such results demonstrate the importance of human oversight during the hyperparameters optimization and weight training phases, capable of analyzing in parallel multiple metrics and data visualization techniques, a process critical to avoid suboptimal solutions when navigating complex hyperspaces

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Metodología de algoritmos meméticos para el problema de ruteo de vehículos con entregas parciales y tiempos de viaje dependientes con ventanas de tiempo

El problema de ruteo de vehículos VRP es uno de los problemas más estudiados en investigación de operaciones, dada su relevancia en los campos del transporte y la logística. En los últimos años ha aumentado el interés en minimizar la contaminación por la emisión de gases efecto invernadero a causa del consumo de combustibles fósiles. El sector transporte representa una parte importante en esas emisiones. En el transporte, situaciones como los embotellamientos en las horas pico, por ejemplo, conducen a una red vial dinámica en la que varían los tiempos de viaje y consecuentemente el consumo de combustible. Por lo anterior el problema de enrutamiento de vehículos con tiempos dependientes TDVRP es una representación más cercana la vida real que los modelos tradicionales de enrutamientos de vehículos, VRP. Por otro lado, el problema de enrutamiento de vehículos con partición de entregas, SDVRP permite asignar múltiples rutas a un mismo cliente, propiciando ahorros en las mismas. El objetivo de esta tesis es desarrollar un método para el uso de los recursos de transporte, con el fin de atender a los clientes de manera eficiente respecto al costo total de la distancia recorrida y al tiempo total de viaje requerido. El problema consiste en programar un recorrido durante un día dividido en intervalos o zonas horarias, con ventanas de tiempo para atender a cada cliente, vehículos homogéneos con capacidad fija Q y un depósito único. Para ello se propone en este trabajo un Algoritmo Memético (MA) capaz de encontrar soluciones que respetan las restricciones del problema, teniendo en cuenta la posibilidad de hacer particiones en las entregas. Mediante el Diseño de Experimentos se evaluó la calidad de las soluciones generadas respecto a un Algoritmo Genético (GA) desarrollado también para el propósito, teniendo como criterio de evaluación el porcentaje de mejores soluciones alcanzado por cada algoritmo. Los experimentos permiten afirmar que el Algoritmo Memético propuesto supera el Algoritmo Genético, resultando más robusto ante cambios en los parámetros de ambos métodos. La solución propuesta representa un modelo más cercano a la realidad de las redes viales y genera rutas tendientes a disminuir la cantidad, recorrido y tiempo de permanencia de los vehículos en la red vial, conllevando a la disminución de las emisiones de gases efecto invernadero.Abstract: The vehicle routing problem VRP is one of the most studied problems in operations research, given its relevance in the fields of transport and logistics. In recent years, interest in minimizing pollution due to the emission of greenhouse gases, as a result of the consumption of fossil fuels, has increased. The transport sector represents an important part of those emissions. In transport, situations such as traffic jams during peak hours, for example, lead to a dynamic road network in which travel times and consequently fuel consumption vary. Therefore, the time dependent vehicle routing problem, TDVRP, is a closer representation of real life than the traditional vehicle routing models, VRP. On the other hand, the split delivery vehicle routing problem, SDVRP, allows assigning multiple routes to the same client, promoting savings in them. The objective of this thesis is to develop a method for the use of transportation resources, in order to serve customers efficiently with regard to the total cost of the distance traveled and the total traveled time required. The problem consists of scheduling a trip for a day which is divided into intervals or time zones, with time windows to serve each customer, homogeneous vehicles with fixed capacity Q and a single deposit. In order to do so, a Memetic Algorithm (MA) is proposed in this work, capable of finding solutions that respect the constraints of the problem, taking into account the possibility of splitting the deliveries. By using Design of Experiments, the quality of the solutions generated by the Memetic Algorithm was evaluated with respect to a Genetic Algorithm (GA) also developed for the purpose, having as the evaluation criterion the percentage of best solutions reached by each algorithm. The experiments show that the proposed memetic algorithm surpasses the genetic algorithm, being more robust to changes in the parameters of both methods The proposed solution represents a model that is closer to the reality of road networks and generates routes that tend to reduce quantity, travel length and time spent by vehicles on the road network, leading to a reduction in greenhouse gas emissions.Maestrí

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

Automation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.postprin