Germán Hernández

A comparison of multiobjective evolutionary algorithms with informed initialization and kuhn-munkres algorithm for the sailor assignment problem

This paper examines the performance of two multiobjective evolutionary algorithms, NSGA-II and SPEA2, with informed initialization on large instances of United States Navys Sailor Assignment Problem. The informed initialization includes in the initial population special solutions obtained by an extension of the Kuhn-Munkres algorithm. The Kuhn-Munkres algorithm, a classical algorithm that solves in

Computing optimal solutions of a linear programming problem with interval type-2 fuzzy constraints

O(n^3)

A Transportation Model with Interval Type-2 Fuzzy Demands and Supplies

time instances of the single valued linear assignment problem, is extended here to render it applicable on single objective instances of the sailor assignment problem obtained using weight vectors to scalarize the natural multiobjective formulation. The Kuhn-Munkres extension is also used to provide a performance benchmark for comparison with the evolutionary algorithms.

Towards a self-stopping evolutionary algorithm using coupling from the past

This paper presents the computation of the set of optimal solutions of a Fuzzy Linear Programming model with constraints that involve uncertainty, by means of Interval Type-2 Fuzzy sets. By applying convex optimization algorithms to a linear programming model with Interval Type-2 fuzzy constraints, an Interval Type-2 fuzzy set of optimal solutions derived from the uncertain constraints of the problem, is obtained. This set of optimal solutions is defined through four boundaries which determine its behavior. Finally, some theoretical considerations are made and explained through an application example.

High-Frequency Trading Strategy Based on Deep Neural Networks

This paper presents a basic transportation model (TM) where its demands and supplies are defined as Interval Type-2 Fuzzy sets (IT2FS). This kind of constraints involves uncertainty to the membership function of a fuzzy set, so we called this model as Interval Type-2 Transportation Model (IT2TM). Using convex optimization techniques, a global solution of this problem can befound. To do so, we define a general model for IT2TM and then we present an application example to illustrate how the algorithm works.

An evolutionary algorithm for fractal coding of binary images

In this paper a stopping criterion for a particular class of evolutionary algorithms is devised. First, a model of a generic evolutionary algorithm using iterated random maps is presented. The model allows the exploration of a connection between coupling from the past, and a stopping criterion for evolutionary algorithms. Accordingly, a method to stop a generic evolutionary algorithm is proposed. Some computational experiments are carried out to test the stopping criterion, using a modified version of coupling from the past. Empirical evidence is shown to support the suitability of the criterion.

A method for solving linear programming models with Interval Type-2 fuzzy constraints

This paper presents a high-frequency strategy based on Deep Neural Networks (DNNs). The DNN was trained on current time (hour and minute), and \( n \)-lagged one-minute pseudo-returns, price standard deviations and trend indicators in order to forecast the next one-minute average price. The DNN predictions are used to build a high-frequency trading strategy that buys (sells) when the next predicted average price is above (below) the last closing price. The data used for training and testing are the AAPL tick-by-tick transactions from September to November of 2008. The best-found DNN has a 66 % of directional accuracy. This strategy yields an 81 % successful trades during testing period.

Computational Visual Analysis of the Order Book Dynamics for Creating High-frequency Foreign Exchange Trading Strategies☆

An evolutionary algorithm is used to search for iterated function systems (IFS) that can encode black and white images. As the number of maps of the IFS that encodes an image cannot be known in advance, a variable-length genotype is used to represent candidate solutions, Accordingly, feasibility conditions of the maps are introduced, and special genetic operators that maintain and control their feasibility are defined, In addition, several similarity measures are used to define different fitness functions for experimentation. The performance of the proposed methods is tested on a set of binary images, and experimental results are reported.

Detecting Informative Patterns in Financial Market Trends Based on Visual Analysis

This paper shows a method for solving linear programming problems that includes Interval Type-2 fuzzy constraints. The proposed method finds an optimal solution in these conditions using convex optimization techniques. Some feasibility conditions are presented, and some interpretation issues are discussed. An introductory example is solved using the proposed method, and its results are described and discussed.

Learning of Natural Trading Strategies on Foreign Exchange High-Frequency Market Data Using Dynamic Bayesian Networks

Abstract This paper presents a Hierarchical Hidden Markov Model used to capture the USD/COP market sentiment dynamics choosing from uptrend or downtrend latent regimes based on observed feature vector realizations calculated from transaction prices and wavelet-transformed order book volume dynamics. The HHMM learned a natural switching buy/uptrend sell/downtrend trading strategy using a training-validation framework over one month of market data. The model was tested on the following two months, and its performance was reported and compared to results obtained from randomly classified market states and a feed-forward Neural Network. This paper also separately assessed the contribution to the models performance of the order book information and the wavelet transformation.