An Approach to Pattern Recognition by Evolutionary Computation

Evolutionary Computation has been inspired by the natural phenomena of evolution. It provides a quite general heuristic, exploiting few basic concepts: reproduction of individuals, variation phenomena that affect the likelihood of survival of individuals, inheritance of parents features by offspring. EC has been widely used in the last years to effectively solve hard, non linear and very complex problems. Among the others, EC–based algorithms have also been used to tackle classification problems. Classification is a process according to which an object is attributed to one of a finite set of classes or, in other words, it is recognized as belonging to a set of equal or similar entities, identified by a label. Most likely, the main aspect of classification concerns the generation of prototypes to be used to recognize unknown patterns. The role of prototypes is that of representing patterns belonging to the different classes defined within a given problem. For most of the problems of practical interest, the generation of such prototypes is a very hard problem, since a prototype must be able to represent patterns belonging to the same class, which may be significantly dissimilar each other. They must also be able to discriminate patterns belonging to classes different from the one that they represent. Moreover, a prototype should contain the minimum amount of information required to satisfy the requirements just mentioned. The research presented in this thesis, has led to the definition of an EC–based framework to be used for prototype generation. The defined framework does not provide for the use of any particular kind of prototypes. In fact, it can generate any kind of prototype once an encoding scheme for the used prototypes has been defined. The generality of the framework can be exploited to develop many applications. The framework has been employed to implement two specific applications for prototype generation. The developed applications have been tested on several data sets and the results compared with those obtained by other approaches previously presented in the literature

The Evolution of Language Universals: Optimal Design and Adaptation

Inquiry into the evolution of syntactic universals is hampered by severe limitations on the available evidence. Theories of selective function nevertheless lead to predictions of local optimaliiy that can be tested scientifically. This thesis refines a diagnostic, originally proposed by Parker and Maynard Smith (1990), for identifying selective functions on this basis and applies it to the evolution of two syntactic universals: (I) the distinction between open and closed lexical classes, and (2) nested constituent structure. In the case of the former, it is argued that the selective role of the closed class items is primarily to minimise the amount of redundancy in the lexicon. In the case of the latter, the emergence of nested phrase structure is argued to have been a by-product of selection for the ability to perform insertion operations on sequences - a function that plausibly pre-dated the emergence of modem language competence. The evidence for these claims is not just that these properties perform plausibly fitness-related functions, but that they appear to perform them in a way that is improbably optimal. A number of interesting findings follow when examining the selective role of the closed classes. In particular, case, agreement and the requirement that sentences have subjects are expected consequences of an optimised lexicon, the theory thereby relating these properties to natural selection for the first time. It also motivates the view that language variation is confined to parameters associated with closed class items, in turn explaining why parameter confiicts fail to arise in bilingualism. The simplest representation of sequences that is optimised for efficient insertions can represent both nested constituent structure and long-distance dependencies in a unified way, thus suggesting that movement is intrinsic to the representation of constituency rather than an 'imperfection'. The basic structure of phrases also follows from this representation and helps to explain the interaction between case and theta assignment. These findings bring together a surprising array of phenomena, reinforcing its correctness as the representational basis of syntactic structures. The diagnostic overcomes shortcomings in the approach of Pinker and Bloom (1990), who argued that the appearance of 'adaptive complexity' in the design of a trait could be used as evidence of its selective function, but there is no reason to expect the refinements of natural selection to increase complexity in any given case. Optimality considerations are also applied in this thesis to filter theories of the nature of unobserved linguistic representations as well as theories of their functions. In this context, it is argued that, despite Chomsky's (1995) resistance to the idea, it is possible to motivate the guiding principles of the Minimalist Program in terms of evolutionary optimisation, especially if we allow the possibility that properties of language were selected for non-communicative functions and that redundancy is sometimes costly rather than beneficial

Railway Research

This book focuses on selected research problems of contemporary railways. The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. The last chapter focuses on modeling railway vehicle dynamics using numerical simulation, where the dynamical models are exploited

Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature

[ES] El futuro de la imagen médica está ligado a la inteligencia artificial. El análisis manual de imágenes médicas es hoy en día una tarea ardua, propensa a errores y a menudo inasequible para los humanos, que ha llamado la atención de la comunidad de Aprendizaje Automático (AA). La Imagen por Resonancia Magnética (IRM) nos proporciona una rica variedad de representaciones de la morfología y el comportamiento de lesiones inaccesibles sin una intervención invasiva arriesgada. Sin embargo, explotar la potente pero a menudo latente información contenida en la IRM es una tarea muy complicada, que requiere técnicas de análisis computacional inteligente. Los tumores del sistema nervioso central son una de las enfermedades más críticas estudiadas a través de IRM. Específicamente, el glioblastoma representa un gran desafío, ya que, hasta la fecha, continua siendo un cáncer letal que carece de una terapia satisfactoria. Del conjunto de características que hacen del glioblastoma un tumor tan agresivo, un aspecto particular que ha sido ampliamente estudiado es su heterogeneidad vascular. La fuerte proliferación vascular del glioblastoma, así como su robusta angiogénesis han sido consideradas responsables de la alta letalidad de esta neoplasia. Esta tesis se centra en la investigación y desarrollo del método Hemodynamic Tissue Signature (HTS): un método de AA no supervisado para describir la heterogeneidad vascular de los glioblastomas mediante el análisis de perfusión por IRM. El método HTS se basa en el concepto de hábitat, que se define como una subregión de la lesión con un perfil de IRM que describe un comportamiento fisiológico concreto. El método HTS delinea cuatro hábitats en el glioblastoma: el hábitat HAT, como la región más perfundida del tumor con captación de contraste; el hábitat LAT, como la región del tumor con un perfil angiogénico más bajo; el hábitat IPE, como la región adyacente al tumor con índices de perfusión elevados; y el hábitat VPE, como el edema restante de la lesión con el perfil de perfusión más bajo. La investigación y desarrollo de este método ha originado una serie de contribuciones enmarcadas en esta tesis. Primero, para verificar la fiabilidad de los métodos de AA no supervisados en la extracción de patrones de IRM, se realizó una comparativa para la tarea de segmentación de gliomas de grado alto. Segundo, se propuso un algoritmo de AA no supervisado dentro de la familia de los Spatially Varying Finite Mixture Models. El algoritmo propone una densidad a priori basada en un Markov Random Field combinado con la función probabilística Non-Local Means, para codificar la idea de que píxeles vecinos tienden a pertenecer al mismo objeto. Tercero, se presenta el método HTS para describir la heterogeneidad vascular del glioblastoma. El método se ha aplicado a casos reales en una cohorte local de un solo centro y en una cohorte internacional de más de 180 pacientes de 7 centros europeos. Se llevó a cabo una evaluación exhaustiva del método para medir el potencial pronóstico de los hábitats HTS. Finalmente, la tecnología desarrollada en la tesis se ha integrado en la plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofrece dos servicios: 1) segmentación de tejidos de glioblastoma, y 2) evaluación de la heterogeneidad vascular del tumor mediante el método HTS. Los resultados de esta tesis han sido publicados en diez contribuciones científicas, incluyendo revistas y conferencias de alto impacto en las áreas de Informática Médica, Estadística y Probabilidad, Radiología y Medicina Nuclear y Aprendizaje Automático. También se emitió una patente industrial registrada en España, Europa y EEUU. Finalmente, las ideas originales concebidas en esta tesis dieron lugar a la creación de ONCOANALYTICS CDX, una empresa enmarcada en el modelo de negocio de los companion diagnostics de compuestos farmacéuticos.[EN] The future of medical imaging is linked to Artificial Intelligence (AI). The manual analysis of medical images is nowadays an arduous, error-prone and often unaffordable task for humans, which has caught the attention of the Machine Learning (ML) community. Magnetic Resonance Imaging (MRI) provides us with a wide variety of rich representations of the morphology and behavior of lesions completely inaccessible without a risky invasive intervention. Nevertheless, harnessing the powerful but often latent information contained in MRI acquisitions is a very complicated task, which requires computational intelligent analysis techniques. Central nervous system tumors are one of the most critical diseases studied through MRI. Specifically, glioblastoma represents a major challenge, as it remains a lethal cancer that, to date, lacks a satisfactory therapy. Of the entire set of characteristics that make glioblastoma so aggressive, a particular aspect that has been widely studied is its vascular heterogeneity. The strong vascular proliferation of glioblastomas, as well as their robust angiogenesis and extensive microvasculature heterogeneity have been claimed responsible for the high lethality of the neoplasm. This thesis focuses on the research and development of the Hemodynamic Tissue Signature (HTS) method: an unsupervised ML approach to describe the vascular heterogeneity of glioblastomas by means of perfusion MRI analysis. The HTS builds on the concept of habitats. A habitat is defined as a sub-region of the lesion with a particular MRI profile describing a specific physiological behavior. The HTS method delineates four habitats within the glioblastoma: the HAT habitat, as the most perfused region of the enhancing tumor; the LAT habitat, as the region of the enhancing tumor with a lower angiogenic profile; the potentially IPE habitat, as the non-enhancing region adjacent to the tumor with elevated perfusion indexes; and the VPE habitat, as the remaining edema of the lesion with the lowest perfusion profile. The research and development of the HTS method has generated a number of contributions to this thesis. First, in order to verify that unsupervised learning methods are reliable to extract MRI patterns to describe the heterogeneity of a lesion, a comparison among several unsupervised learning methods was conducted for the task of high grade glioma segmentation. Second, a Bayesian unsupervised learning algorithm from the family of Spatially Varying Finite Mixture Models is proposed. The algorithm integrates a Markov Random Field prior density weighted by the probabilistic Non-Local Means function, to codify the idea that neighboring pixels tend to belong to the same semantic object. Third, the HTS method to describe the vascular heterogeneity of glioblastomas is presented. The HTS method has been applied to real cases, both in a local single-center cohort of patients, and in an international retrospective cohort of more than 180 patients from 7 European centers. A comprehensive evaluation of the method was conducted to measure the prognostic potential of the HTS habitats. Finally, the technology developed in this thesis has been integrated into an online open-access platform for its academic use. The ONCOhabitats platform is hosted at https://www.oncohabitats.upv.es, and provides two main services: 1) glioblastoma tissue segmentation, and 2) vascular heterogeneity assessment of glioblastomas by means of the HTS method. The results of this thesis have been published in ten scientific contributions, including top-ranked journals and conferences in the areas of Medical Informatics, Statistics and Probability, Radiology & Nuclear Medicine and Machine Learning. An industrial patent registered in Spain, Europe and EEUU was also issued. Finally, the original ideas conceived in this thesis led to the foundation of ONCOANALYTICS CDX, a company framed into the business model of companion diagnostics for pharmaceutical compounds.[CA] El futur de la imatge mèdica està lligat a la intel·ligència artificial. L'anàlisi manual d'imatges mèdiques és hui dia una tasca àrdua, propensa a errors i sovint inassequible per als humans, que ha cridat l'atenció de la comunitat d'Aprenentatge Automàtic (AA). La Imatge per Ressonància Magnètica (IRM) ens proporciona una àmplia varietat de representacions de la morfologia i el comportament de lesions inaccessibles sense una intervenció invasiva arriscada. Tanmateix, explotar la potent però sovint latent informació continguda a les adquisicions de IRM esdevé una tasca molt complicada, que requereix tècniques d'anàlisi computacional intel·ligent. Els tumors del sistema nerviós central són una de les malalties més crítiques estudiades a través de IRM. Específicament, el glioblastoma representa un gran repte, ja que, fins hui, continua siguent un càncer letal que manca d'una teràpia satisfactòria. Del conjunt de característiques que fan del glioblastoma un tumor tan agressiu, un aspecte particular que ha sigut àmpliament estudiat és la seua heterogeneïtat vascular. La forta proliferació vascular dels glioblastomes, així com la seua robusta angiogènesi han sigut considerades responsables de l'alta letalitat d'aquesta neoplàsia. Aquesta tesi es centra en la recerca i desenvolupament del mètode Hemodynamic Tissue Signature (HTS): un mètode d'AA no supervisat per descriure l'heterogeneïtat vascular dels glioblastomas mitjançant l'anàlisi de perfusió per IRM. El mètode HTS es basa en el concepte d'hàbitat, que es defineix com una subregió de la lesió amb un perfil particular d'IRM, que descriu un comportament fisiològic concret. El mètode HTS delinea quatre hàbitats dins del glioblastoma: l'hàbitat HAT, com la regió més perfosa del tumor amb captació de contrast; l'hàbitat LAT, com la regió del tumor amb un perfil angiogènic més baix; l'hàbitat IPE, com la regió adjacent al tumor amb índexs de perfusió elevats, i l'hàbitat VPE, com l'edema restant de la lesió amb el perfil de perfusió més baix. La recerca i desenvolupament del mètode HTS ha originat una sèrie de contribucions emmarcades a aquesta tesi. Primer, per verificar la fiabilitat dels mètodes d'AA no supervisats en l'extracció de patrons d'IRM, es va realitzar una comparativa en la tasca de segmentació de gliomes de grau alt. Segon, s'ha proposat un algorisme d'AA no supervisat dintre de la família dels Spatially Varying Finite Mixture Models. L'algorisme proposa un densitat a priori basada en un Markov Random Field combinat amb la funció probabilística Non-Local Means, per a codificar la idea que els píxels veïns tendeixen a pertànyer al mateix objecte semàntic. Tercer, es presenta el mètode HTS per descriure l'heterogeneïtat vascular dels glioblastomas. El mètode HTS s'ha aplicat a casos reals en una cohort local d'un sol centre i en una cohort internacional de més de 180 pacients de 7 centres europeus. Es va dur a terme una avaluació exhaustiva del mètode per mesurar el potencial pronòstic dels hàbitats HTS. Finalment, la tecnologia desenvolupada en aquesta tesi s'ha integrat en una plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofereix dos serveis: 1) segmentació dels teixits del glioblastoma, i 2) avaluació de l'heterogeneïtat vascular dels glioblastomes mitjançant el mètode HTS. Els resultats d'aquesta tesi han sigut publicats en deu contribucions científiques, incloent revistes i conferències de primer nivell a les àrees d'Informàtica Mèdica, Estadística i Probabilitat, Radiologia i Medicina Nuclear i Aprenentatge Automàtic. També es va emetre una patent industrial registrada a Espanya, Europa i els EEUU. Finalment, les idees originals concebudes en aquesta tesi van donar lloc a la creació d'ONCOANALYTICS CDX, una empresa emmarcada en el model de negoci dels companion diagnostics de compostos farmacèutics.En este sentido quiero agradecer a las diferentes instituciones y estructuras de financiación de investigación que han contribuido al desarrollo de esta tesis. En especial quiero agradecer a la Universitat Politècnica de València, donde he desarrollado toda mi carrera acadèmica y científica, así como al Ministerio de Ciencia e Innovación, al Ministerio de Economía y Competitividad, a la Comisión Europea, al EIT Health Programme y a la fundación Caixa ImpulseJuan Albarracín, J. (2020). Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149560TESI

Automated development of clinical prediction models using genetic programming

Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Genetic programming is a general methodology, the specific implementation of which requires development of several different specific elements such as problem representation, fitness, selection and genetic variation. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this work was to develop a genetic programming approach for survival analysis and demonstrate its utility for the automatic development of clinical prediction models using cardiovascular disease as a case study. We developed a tree-based untyped steady-state genetic programming approach for censored longitudinal data, comparing its performance to the de facto statistical method—Cox regression—in the development of clinical prediction models for the prediction of future cardiovascular events in patients with symptomatic and asymptomatic cardiovascular disease, using large observational datasets. We also used genetic programming to examine the prognostic significance of different risk factors together with their non-linear combinations for the prognosis of health outcomes in cardiovascular disease. These experiments showed that Cox regression and the developed steady-state genetic programming approach produced similar results when evaluated in common validation datasets. Despite slight relative differences, both approaches demonstrated an acceptable level of discriminative and calibration at a range of times points. Whilst the application of genetic programming did not provide more accurate representations of factors that predict the risk of both symptomatic and asymptomatic cardiovascular disease when compared with existing methods, genetic programming did offer comparable performance. Despite generally comparable performance, albeit in slight favour of the Cox model, the predictors selected for representing their relationships with the outcome were quite different and, on average, the models developed using genetic programming used considerably fewer predictors. The results of the genetic programming confirm the prognostic significance of a small number of the most highly associated predictors in the Cox modelling; age, previous atherosclerosis, and albumin for secondary prevention; age, recorded diagnosis of ’other’ cardiovascular disease, and ethnicity for primary prevention in patients with type 2 diabetes. When considered as a whole, genetic programming did not produce better performing clinical prediction models, rather it utilised fewer predictors, most of which were the predictors that Cox regression estimated be most strongly associated with the outcome, whilst achieving comparable performance. This suggests that genetic programming may better represent the potentially non-linear relationship of (a smaller subset of) the strongest predictors. To our knowledge, this work is the first study to develop a genetic programming approach for censored longitudinal data and assess its value for clinical prediction in comparison with the well-known and widely applied Cox regression technique. Using empirical data this work has demonstrated that clinical prediction models developed by steady-state genetic programming have predictive ability comparable to those developed using Cox regression. The genetic programming models were more complex and thus more difficult to validate by domain experts, however these models were developed in an automated fashion, using fewer input variables, without the need for domain specific knowledge and expertise required to appropriately perform survival analysis. This work has demonstrated the strong potential of genetic programming as a methodology for automated development of clinical prediction models for diagnostic and prognostic purposes in the presence of censored data. This work compared untuned genetic programming models that were developed in an automated fashion with highly tuned Cox regression models that was developed in a very involved manner that required a certain amount of clinical and statistical expertise. Whilst the highly tuned Cox regression models performed slightly better in validation data, the performance of the automatically generated genetic programming models were generally comparable. The comparable performance demonstrates the utility of genetic programming for clinical prediction modelling and prognostic research, where the primary goal is accurate prediction. In aetiological research, where the primary goal is to examine the relative strength of association between risk factors and the outcome, then Cox regression and its variants remain as the de facto approach

A Framework for Estimating the Applicability of GAs for Real‐World Optimization Problems

This paper introduces a methodology for estimating the applicability of a particular Genetic Algorithm (GA) configuration for an arbitrary optimization problem based on run-time data. GAs are increasingly employed to solve complex real-world optimization problems featuring ill-behaved search spaces (e.g., non-continuous, non-convex, non-differentiable) for which traditional algorithms fail. The quality of the optimal solution (i.e., the fitness value of the global optimum) is typically unknown in a real-world problem, making it hard to assess the absolute performance of an algorithm which is being applied to that problem. In other words, with a solution provided by a GA run, there generally lacks a method or a theory to measure how good the solution is. Although many researchers applying GAs have provided experimental results showing their successful applications, those are merely averaged-out, \emph{ad hoc} results. The results cannot represent nor guarantee the usability of the best solutions obtained from a single GA run since the solutions can be very different for each run. Therefore, it is desirable to provide a formalized measurement to estimate the applicability of GAs to real-world problems. This work extends our earlier work on the convergence rate, and proposes an evaluation metric to quantify the applicability of GAs. Through this metric, a degree of convergence can be obtained after each GA run so that researchers and practitioners are able to obtain certain information about the relation between the best solution and all of the feasible solutions. To support the proposed evaluation metric, a series of theorems are formulated from the theory of matrices. Moreover, several experiments are conducted to validate the metric

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways