Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.     

Efficient wavelet-based ECG processing for single-lead FHR extraction

This paper describes a novel model for fetal heart rate (FHR) monitoring from single-lead mother?s abdomen ECG (AECG) measurements. This novel method is divided in two stages: the first step consists on a one-step wavelet-based preprocessing for simultaneous baseline and high-frequency noise suppression, while the second stage efficiently detects fetal QRS complexes allowing FHR monitoring. The presented structure has been simplified as much as possible, in order to reduce computational cost and thus enable possible custom hardware implementations. Moreover, the proposed scheme and its fixed-point modeling have been tested using real abdominal ECG signals, which allow the validation of the presented approach and provide high accuracy.     

Cartogram visualization for nonlinear manifold learning models

Real-world applications of multivariate data analysis often stumble upon the barrier of interpretability. Simple data analysis methods are usually easy to interpret, but they risk providing poor data models. More involved methods may instead yield faithful data models, but limited interpretability. This is the case of linear and nonlinear methods for multivariate data visualization through dimensionality reduction. Even though the latter have provided some of the most exciting visualization developments, their practicality is hindered by the difficulty of explaining them in an intuitive manner. The interpretability, and therefore the practical applicability, of data visualization through nonlinear dimensionality reduction (NLDR) methods would improve if, first, we could accurately calculate the distortion introduced by these methods in the visual representation and, second, if we could faithfully reintroduce this distortion into such representation. In this paper, we describe a technique for the reintroduction of the distortion into the visualization space of NLDR models. It is based on the concept of density-equalizing maps, or cartograms, recently developed for the representation of geographic information. We illustrate it using Generative Topographic Mapping (GTM), a nonlinear manifold learning method that can provide both multivariate data visualization and a measure of the local distortion that the model generates. Although illustrated here with GTM, it could easily be extended to other NLDR visualization methods, provided a local distortion measure could be calculated. It could also serve as a guiding tool for interactive data visualization.     

Service oriented evolutionary algorithms

This work presents a service oriented architecture for evolutionary algorithms, and an implementation of this architecture using a specific technology (called OSGiLiath). Service oriented architecture is a computational paradigm where users interact using services to increase the integration between systems. The presented abstract architecture is formed by loosely coupled, highly configurable and language-independent services. As an example of an implementation of this architecture, a complete process development using a specific service oriented technology is explained. With this implementation, less effort than classical development in integration, distribution mechanisms and execution time management has been attained. In addition, steps, ideas, advantages and disadvantages, and guidelines to create service oriented evolutionary algorithms are presented. Using existing software, or from scratch, researchers can create services to increase the interoperability in this area.     

Mechanical resistance properties of gravel used in subsurface flow constructed wetlands: implications for clogging

Gravel constitutes the filter medium in subsurface flow constructed wetlands (SSF CWs) and its porosity and hydraulic conductivity decrease over time (clogging), limiting the lifespan of the systems. Using gravel of poor quality accelerates clogging in wetlands. In this study, gravel samples from six different wetland systems were compared with regards to their mineral composition and mechanical resistance properties. Results showed that both mineralogy and texture are related to mechanical resistance. Accordingly, gravel with high content of quartz (> 80%) showed a lower percentage of broken particles (0.18-1.03%) than those with lower content of quartz (2.42-4.56% media broken). Although granite is formed by high durability minerals, its non-uniform texture results in a lower resistance to abrasion (ca. 10% less resistance than calcareous gravel). Therefore, it is recommended to use gravels composed mainly of quartz or, when it is not available, limestone gravels (rounded and uniform) are recommended instead. The resistance to abrasion (LAA test) seems to be a good indicator to determine the mechanical properties of gravels used in CWs. It is recommended to use gravels with LAA below 30% in order to avoid a rapid clogging due to gravel crumbling and subsequent mineral solids accumulation.     

Advance human–machine interface automatic evaluation

The need for accessibility evaluation tools is motivated by several endogenous and exogenous reasons coming from the end user (the designer and the developer) and companies releasing information systems. Existing evaluation tools mainly concentrate on examining the code of Web pages: Web pages more and more frequently contain non-HTML parts that entirely escape from being treated by existing techniques. This is the case of the advanced human–machine interface (AHMI), a piece of software programmed in C/C++, used for controlling the advanced flight management system in the aircraft cockpit. Studying this new user interface (UI) requires a structured approach to evaluate and validate AHMI designs. The goal in this work is to develop an evaluation tool to automate the process of evaluating the AHMI. The method addresses: support of multiple bases of guidelines (accessibility or usability or both) on-demand (partial or total evaluation), with different levels of details (a presentation for developers and for those responsible for certifying accessibility). The method goes a step toward the automatic evaluation of UI containing non-HTML parts.     

Analytical formulation to compute QFT bounds: The envelope method

This paper describes an analytical formulation to compute quantitative feedback theory (QFT) bounds in one‐degree‐of‐freedom feedback control problems. The new approach is based on envelope curves and shows that a QFT control specification can be expressed as a family of circumferences. Then, the controller bound is defined by the envelope curve of this family and can be obtained as an analytical function. This offers the possibility of studying the QFT bounds in an analytical way with several useful properties. Gridding methods are avoided, resulting in a lower computational effort procedure. The new formulation improves the accuracy of previous methods and allows the designer to calculate multivalued bounds. Copyright © 2009 John Wiley & Sons, Ltd.     

Molecular modeling of tricyclic compounds with anilino substituents and their intercalation complexes with DNA sequences

Although 9-anilinoacridines are among the best studied antitumoral intercalators, there are few studies about the effect of isosteric substitution of a benzene moiety for a heterocycle ring in the acridine framework. According to these studies, this approach may lead to effective cytotoxic agents, but good cytotoxic activity depends on structural requirements in the aniline ring which differ from those in 9-anilinoacridines. The present paper deals with molecular modeling studies of some 9-anilino substituted tricyclic compounds and their intercalation complexes (in various DNA sequences) resulting from docking the compounds into various DNA sequences. As expected, the isosteric substitution in 9-anilinoacridines influences the LUMO energy values and orbital distribution, the dipole moment, electrostatic charges and the conformation of the anilino ring. Other important differences are observed during the docking studies, for example, changes in the spatial arrangement of the tricyclic nucleus and the anilino ring at the intercalation site. Semiempirical calculations of the intercalation complexes show that the isosteric replacement of a benzene ring in the acridine nucleus affects not only DNA affinity but also base pair selectivity. These findings explain, at least partially, the different structural requirements observed in several 9-anilino substituted tricyclic compounds for cytotoxic activity. Thus, the data presented here may guide the rational design of new agents with different DNA binding properties and/or a cytotoxic profile by isosteric substitution of known intercalators.     

Multicriteria decisions on interdependent infrastructure transportation projects using an evolutionary-based framework

When evaluating transportation infrastructure projects and determining which of them will be carried out from a set of projects and given a budget constraint, several criteria need to be considered in the decision. Standard evaluation practices imply the aggregation of impacts into one utility function which is later optimized. Nevertheless these techniques used for translation of different measuring units into monetary terms are highly controversial. Multicriteria techniques can explicitly deal with different measuring units, however, they are not suitable to model interdependence relationships of projects that share a common characteristic (same route, location or target population, for instance). In this research we model this transportation planning problem, the multi-objective transportation infrastructure project selection problem (MTIPSP), as a constrained multi-objective optimization problem with quadratic objective functions, using a variation of the multi-objective 0–1 knapsack problem plus some additional constraints. Given the combinatorial nature of the problem, an evolutionary-based framework is used for the identification of Pareto solutions, and later, those with non-attractive properties are filtered using a Knee Identification Procedure. The final selection of the projects portfolio is made using a well known multicriteria decision aid method and including the decision makers’ preferences based on the existing context.