Catálogo de publicaciones - libros

Quantum effects are a natural phenomenon and just like evolution, or immune systems, can serve as an inspiration for the design of computing algorithms. This study illustrates how a quantum-inspired evolutionary algorithm can be constructed and examines the utility of the resulting algorithm on Option Pricing model calibration. The results from the algorithm are shown to be robust and comparable to those of other algorithms.

Due to increasing complexity and non-convexity of financial engineering problems, biologically inspired heuristic algorithms gained significant importance especially in the area of financial decision optimization. In this paper, the stochastic scenario-based risk-return portfolio optimization problem is analyzed and solved with an evolutionary computation approach. The advantage of applying this approach is the creation of a common framework for an arbitrary set of loss distribution-based risk measures, regardless of their underlying structure. Numerical results for three of the most commonly used risk measures conclude the paper.

In this paper, an evolutionary approach to portfolio optimization is proposed. In the approach, various risk measures are introduced instead of the classic risk measure defined by variance. In order to build the risk-optimal portfolio, three evolutionary algorithms based on evolution strategies are proposed. Evaluations of the approach is performed on financial time series from the Warsaw Stock Exchange.

The alue-t-isk approach has been used for measuring and controlling the market risks in financial institutions. Studies show that the -distribution is more suited to representing the financial asset returns in VaR calculations than the commonly used normal distribution. The frequency of extremely positive or extremely negative financial asset returns is higher than that is suggested by normal distribution. Such a leptokurtic distribution can better be approximated by a -distribution. The aim of this study is to asses the performance of a real coded Genetic Algorithm (GA) with Evolutionary Strategies (ES) approach for Maximum Likelihood (ML) parameter estimation. Using Monte Carlo (MC) simulations, we compare the test results of VaR simulations using the -distribution, whose optimal parameters are generated by the Evolutionary Algorithms (EAs), to that of the normal distribution. It turns out that the VaR figures calculated with the assumption of normal distribution significantly understate the VaR figures computed from the actual historical distribution at high confidence levels. On the other hand, for the same confidence levels, the VaR figures calculated with the assumption of -distribution are very close to the results found using the actual historical distribution. Finally, in order to speed up the MC simulation technique, which is not commonly preferred in financial applications due to its time consuming algorithm, we implement a parallel version of it.

A Kalman-Filtered Feature-space approach is taken to forecast changes in the ISEQ (Irish Stock Exchange Equity Overall) Index using the previous five days’ lagged returns solely as inputs. The resulting model is tantamount to a time-varying (adaptive) technical trading rule, one which achieves an out-of-sample Sharpe (’reward-to-variability’) Ratio far superior to the ’buy-and-hold’ strategy and its popular ’crossing moving-average’ counterparts. The approach is contrasted to Recurrent Neural Network models and with other previous attempts to combine Kalman-Filtering concepts with (more traditional) Multi-layer Perceptron Network models. The new method proposed is found to be simple to implement, and, based on preliminary results presented here, might be expected to perform well for this type of problem.

This paper describes an application of evolutionary algorithms to the predictive modelling of customer behaviour in a business environment. Predictive models are represented as fuzzy rule bases, which allows for intuitive human interpretability of the results obtained, while providing satisfactory accuracy. An empirical case study is presented to show the effectiveness of the approach.

In this paper we describe results of a modified Particle Swarm Optimization (PSO) algorithm which has been applied to two image analysis tasks. In the former, accurate region-based segmentation is obtained by analyzing the cumulative results of several runs of the algorithm. In the latter, the fast-convergence properties of the algorithm are used to accurately locate and track an object of interest in real time.

In this paper a parallel software system based on Differential Evolution for the registration of images is designed, implemented and tested on a set of 2–D remotely sensed images on two problems, i.e. mosaicking and changes in time. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, without any need for setting control points. A coarse–grained distributed version is implemented on a cluster of personal computers.

Accurate harmonic estimation is the foundation to ensure a reliable power quality environment in a power system. This paper presents a new algorithm based on a Group Search Optimiser (GSO) to estimate the harmonic components presented in a voltage or current waveform. The structure of harmonic estimation is represented as linear in amplitude and non-linear in phase. The proposed algorithm takes advantage of this feature and estimates amplitudes and phases of harmonics by a linear Least Squared (LS) algorithm and a non-linear GSO-based method respectively. The improved estimation accuracy is demonstrated in this paper in comparison with that of the conventional Discrete Fourier Transform (DFT) and Genetic Algorithms (GAs). Moreover, the performance is still satisfactory even in simulations with the presence of inter-harmonics and frequency deviation.

An evolvable hardware (EHW) architecture for high-speed pattern recognition has been proposed. For a complex face image recognition task, the system demonstrates (in simulation) an accuracy of 96.25% which is better than previously proposed EHW architectures. In contrast to previous approaches, this architecture is designed for online evolution. Incremental evolution and high level modules have been utilized in order to make the evolution feasible.