JavaEvA : a Java based framework for Evolutionary Algorithms

Das Softwarepaket JavaEvA (eine Java Implementierung Evolutionärer Algorithmen) ist ein allgemeines modulares Framework für Optimierungsalgorithmen basierend auf einer Client-Server Architektur, das geeignet ist eine Vielzahl von Optimierungsproblemen zu lösen. Das Paket wurde mit dem Schwerpunkt entwickelt neue Verfahren im Bereich der Evolutionären Algorithmen einfach entwickeln und testen zu können und diese Verfahren letztlich in praktischen Anwendungen anzuwenden. JavaEvA beinhaltet Implementierungen der üblichen Evolutionären Verfahren wie zum Beispiel Genetische Algorithmen, die CHC Adaptive Search, Population Based Incremental Learning, Evolutionsstrategien, Modellunterstützte Evolutionsstrategien, Genetisches Programmieren und Grammatical Evolution. Zusätzlich erlaubt es das modulare Framework von JavaEvA eigene eventuell problemspezifische Optimierungsmodule zu ergänzen und mit den implementieren Verfahren zu vergleichen. Das JavaEvA Paket benutzt ein generisches Verfahren zur GUI Generierung und erlaubt so einen einfachen Objektorientierten Zugang zu allen relevanten Parametern eines Evolutionären Algorithmus. Das gleiche Verfahren generiert auch entsprechende GUI Elemente für neu entwickelte Methoden und vereinfacht so den Aufwand bei der Entwicklung neuer Methoden erheblich. Zusätzlich besteht die Möglichkeit spezialisiere GUI Elemente für einzelne Objekte zu entwickeln und in das bestehende System zu integrieren, um die Benutzerfreundlichkeit weiter zu erhöhen. Da es uns unmöglich ist jedwede potenzielle Anwendung oder Optimierungsproblem zu antizipieren, ist es aus praktischen Gründen fast immer nötig eigene Implementierungen des jeweiligen Anwendungsproblems zu erstellen. Um diesen Vorgang zu erleichtern bietet diese Anleitung zusätzliche Beispiele mit detaillierten Beschreibungen, wie man ein eigenes Problem implementieren kann und JavaEvA lediglich als Optimierungstoolbox integriert. Auf diese Weise behält ihre jeweilige Anwendung die vollständige Kontrolle über die verwendeten Verfahren und die anwendungsspezifische Darstellung der Optimierungsergebnisse.The package JavaEvA (a Java implementation of Evolutionary Algorithms) is a general modular framework with an inherent client server structure to solve practical optimization problems. This package was especially designed to test and develop new approaches for Evolutionary Algorithms and to utilize them in real-world applications. JavaEvA already provides implementations of the most common Evolutionary Algorithms, like Genetic Algorithms, CHC Adaptive Search, Population Based Incremental Learning, Evolution Strategies, Model-Assisted Evolution Strategies, Genetic Programming and Grammatical Evolution. In addition the modular framework of JavaEvA allows everyone to add their own optimization modules to meet their specific requirements. The JavaEvA package uses a generic GUI framework that allows GUI access to any member of a class if get and set methods are provided and an editor is defined for the given data type. This approach allows very fast development cycles, since hardly any additional effort is necessary for implementing GUI elements, while still at the same time user specific GUI elements can be developed and integrated to increase usability. Since we cannot anticipate specific optimization problem and requirements, it is necessary for users to define their optimization problem. Therefore, we provide an additional framework and explain how one can include JavaEvA in an existing Java project or how one can implement ones own optimization problem and optimize it by using JavaEvA. This gives users total control of the optimization algorithms used

Image Segmentation and Analysis for Automated Classification of Traumatic Pelvic Injuries

In the past decades, technical advances have allowed for the collection and storage of more types and larger quantities of medical data. The increase in the volume of existing medical data has increased the need for processing and analyzing such data. Medical data holds information that is invaluable for diagnostic as well as treatment planning purposes. Presently, a large portion of the data is not optimally used towards medical decisions because information contained in the data is inaccessible through simple human inspection, or traditional computational methods. In the field of trauma medicine, where caregivers are frequently confronted with situations where they need to make rapid decisions based on large amounts of information, the need for reliable, fast and automated computational methods for decision support systems is stringent. Such methods could process and analyze, in a timely fashion, all available medical data and provide caretakers with recommendations/predictions for both patient diagnostic and treatment planning. Presently however, even extracting features that are known to be useful for diagnosis, like presence and location of hemorrhage and fracture, is not easily achievable in automatic manner. Trauma is the main cause of death among Americans age 40 and younger; hence, it has become a national priority. A computer-aided decision making system capable of rapidly analyzing all data available for a patient and forming reliable recommendations for physicians can greatly impact the quality of care provided to patients. Such a system would also reduce the overall costs involved in patient care as it helps in optimizing the decisions, avoiding unnecessary procedures, and customizing treatments for individual patients. Among different types of trauma with a high impact on the lives of Americans, traumatic pelvic injuries, which often occur in motor vehicle accidents and in falls, have had a tremendous toll on both human lives and healthcare costs in the United States. The present project has developed automated computational methods and algorithms to analyze pelvic CT images and extract significant features describing the severity of injuries. Such a step is of great importance as every CT scan consists of tens of slices that need to be closely examined. This method can automatically extract information hidden in CT images and therefore reduce the time of the examination. The method identifies and signals areas of potential abnormality and allows the user to decide upon the action to be taken (e.g. further examination of the image and/or area and neighboring images in the scan). The project also initiates the design of a system that combines the features extracted from biomedical signals and images with information such as injury scores, injury mechanism and demographic information in order to detect the presence and the severity of Traumatic Pelvic Injuries and to provide recommendations for diagnosis and treatment. The recommendations are provided in form of grammatical rules, allowing physicians to explore the reasoning behind these assessments

Interpretable Dimensionally-Consistent Feature Extraction from Electrical Network Sensors

International audienceElectrical power networks are heavily monitored systems, requiring operators to perform intricate information synthesis before understanding the underlying network state. Our study aims at helping this synthesis step by automatically creating features from the sensor data. We propose a supervised feature extraction approach using a grammar-guided evolution, which outputs interpretable and dimensionally consistent features. Operations restrictions on dimensions are introduced in the learning process through context-free grammars. They ensure coherence with physical laws, dimensional-consistency, and also introduce technical expertise in the created features. We compare our approach to other state-of-the-art feature extraction methods on a real dataset taken from the French electrical network sensors

Using Dimensional Aware Genetic Programming to find interpretable Dispatching Rules for the Job Shop Scheduling Problem

Dispatching Rules (DRs) have been used in several applications in manufacturing systems. They assign priority to jobs in a queue choosing the next job to be executed. As they are challenging to design, genetic programming (GP) is being used to find better performative DRs. In GP, several different DRs are evolved, and due to some operations and selection processes inspired in nature, the DRs improve. However, little research has been done in trying to reach small and interpretable DRs. Usually, these generated expressions tend to become extremely large, with a couple of hundred terms or more. This work will innovate by using CFG (context-free grammars) methods, particularly CFG-GP and GE (Grammar Evolution), for reaching DRs which are dimensional aware. These methods will be compared as they have several distinct characteristics and were never used for this problem. The objective is that by forcing the syntax of the DRs to be correct, it will be possible to reach smaller and more interpretable DRs. Furthermore, an enumerator was made that found the best possible expression for a small DRs size, which will serve as a baseline to evaluate how well the different algorithms can explore these spaces and give the best possible DRs for a specific size. The results show a significant performance improvement in using DAGP methods for this problem. Moreover, GP/GE and CFG-GP can explore the small DRs optimally or close to optimally, managing to find the best small DRs

Evolutionary design of deep neural networks

Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of the topology of artificial neural networks, with most works focusing on very simple architectures. However, times have changed, and nowadays convolutional neural networks are the industry and academia standard for solving a variety of problems, many of which remained unsolved before the discovery of this kind of networks. Convolutional neural networks involve complex topologies, and the manual design of these topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to use neuroevolution in order to evolve the architecture of convolutional neural networks. To do so, we have decided to try two different techniques: genetic algorithms and grammatical evolution. We have implemented a niching scheme for preserving the genetic diversity, in order to ease the construction of ensembles of neural networks. These techniques have been validated against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%, and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275. Both results have proven very competitive when compared with the state of the art. Also, in all cases, ensembles have proven to perform better than individual models. Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced in 2017, which includes more samples and a set of letters for character recognition. Results have shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures can be reused across domains with similar characteristics. In summary, neuroevolution is an effective approach for automatically designing topologies for convolutional neural networks. However, it still remains as an unexplored field due to hardware limitations. Current advances, however, should constitute the fuel that empowers the emergence of this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917. This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca

Studying Evolutionary Change: Transdisciplinary Advances in Understanding and Measuring Evolution

Evolutionary processes can be found in almost any historical, i.e. evolving, system that erroneously copies from the past. Well studied examples do not only originate in evolutionary biology but also in historical linguistics. Yet an approach that would bind together studies of such evolving systems is still elusive. This thesis is an attempt to narrowing down this gap to some extend. An evolving system can be described using characters that identify their changing features. While the problem of a proper choice of characters is beyond the scope of this thesis and remains in the hands of experts we concern ourselves with some theoretical as well data driven approaches. Having a well chosen set of characters describing a system of different entities such as homologous genes, i.e. genes of same origin in different species, we can build a phylogenetic tree. Consider the special case of gene clusters containing paralogous genes, i.e. genes of same origin within a species usually located closely, such as the well known HOX cluster. These are formed by step- wise duplication of its members, often involving unequal crossing over forming hybrid genes. Gene conversion and possibly other mechanisms of concerted evolution further obfuscate phylogenetic relationships. Hence, it is very difficult or even impossible to disentangle the detailed history of gene duplications in gene clusters. Expanding gene clusters that use unequal crossing over as proposed by Walter Gehring leads to distinctive patterns of genetic distances. We show that this special class of distances helps in extracting phylogenetic information from the data still. Disregarding genome rearrangements, we find that the shortest Hamiltonian path then coincides with the ordering of paralogous genes in a cluster. This observation can be used to detect ancient genomic rearrangements of gene clus- ters and to distinguish gene clusters whose evolution was dominated by unequal crossing over within genes from those that expanded through other mechanisms. While the evolution of DNA or protein sequences is well studied and can be formally described, we find that this does not hold for other systems such as language evolution. This is due to a lack of detectable mechanisms that drive the evolutionary processes in other fields. Hence, it is hard to quantify distances between entities, e.g. languages, and therefore the characters describing them. Starting out with distortions of distances, we first see that poor choices of the distance measure can lead to incorrect phylogenies. Given that phylogenetic inference requires additive metrics we can infer the correct phylogeny from a distance matrix D if there is a monotonic, subadditive function ζ such that ζ^−1(D) is additive. We compute the metric-preserving transformation ζ as the solution of an optimization problem. This result shows that the problem of phylogeny reconstruction is well defined even if a detailed mechanistic model of the evolutionary process is missing. Yet, this does not hinder studies of language evolution using automated tools. As the amount of available and large digital corpora increased so did the possibilities to study them automatically. The obvious parallels between historical linguistics and phylogenetics lead to many studies adapting bioinformatics tools to fit linguistics means. Here, we use jAlign to calculate bigram alignments, i.e. an alignment algorithm that operates with regard to adjacency of letters. Its performance is tested in different cognate recognition tasks. Using pairwise alignments one major obstacle is the systematic errors they make such as underestimation of gaps and their misplacement. Applying multiple sequence alignments instead of a pairwise algorithm implicitly includes more evolutionary information and thus can overcome the problem of correct gap placement. They can be seen as a generalization of the string-to-string edit problem to more than two strings. With the steady increase in computational power, exact, dynamic programming solutions have become feasible in practice also for 3- and 4-way alignments. For the pairwise (2-way) case, there is a clear distinction between local and global alignments. As more sequences are consid- ered, this distinction, which can in fact be made independently for both ends of each sequence, gives rise to a rich set of partially local alignment problems. So far these have remained largely unexplored. Thus, a general formal frame- work that gives raise to a classification of partially local alignment problems is introduced. It leads to a generic scheme that guides the principled design of exact dynamic programming solutions for particular partially local alignment problems

Multi-objective particle swarm optimization for the structural design of concentric tube continuum robots for medical applications

Concentric tube robots belong to the class of continuum robotic systems whose morphology is described by continuous tangent curvature vectors. They are composed of multiple, interacting tubes nested inside one another and are characterized by their inherent flexibility. Concentric tube continuum robots equipped with tools at their distal end have high potential in minimally invasive surgery. Their morphology enables them to reach sites within the body that are inaccessible with commercial tools or that require large incisions. Further, they can be deployed through a tight lumen or follow a nonlinear path. Fundamental research has been the focus during the last years bringing them closer to the operating room. However, there remain challenges that require attention. The structural synthesis of concentric tube continuum robots is one of these challenges, as these types of robots are characterized by their large parameter space. On the one hand, this is advantageous, as they can be deployed in different patients, anatomies, or medical applications. On the other hand, the composition of the tubes and their design is not a straightforward task but one that requires intensive knowledge of anatomy and structural behavior. Prior to the utilization of such robots, the composition of tubes (i.e. the selection of design parameters and application-specific constraints) must be solved to determine a robotic design that is specifically targeted towards an application or patient. Kinematic models that describe the change in morphology and complex motion increase the complexity of this synthesis, as their mathematical description is highly nonlinear. Thus, the state of the art is concerned with the structural design of these types of robots and proposes optimization algorithms to solve for a composition of tubes for a specific patient case or application. However, existing approaches do not consider the overall parameter space, cannot handle the nonlinearity of the model, or multiple objectives that describe most medical applications and tasks. This work aims to solve these fundamental challenges by solving the parameter optimization problem by utilizing a multi-objective optimization algorithm. The main concern of this thesis is the general methodology to solve for patient- and application-specific design of concentric tube continuum robots and presents key parameters, objectives, and constraints. The proposed optimization method is based on evolutionary concepts that can handle multiple objectives, where the set of parameters is represented by a decision vector that can be of variable dimension in multidimensional space. Global optimization algorithms specifically target the constrained search space of concentric tube continuum robots and nonlinear optimization enables to handle the highly nonlinear elasticity modeling. The proposed methodology is then evaluated based on three examples that include cooperative task deployment of two robotic arms, structural stiffness optimization under the consideration of workspace constraints and external forces, and laser-induced thermal therapy in the brain using a concentric tube continuum robot. In summary, the main contributions are 1) the development of an optimization methodology that describes the key parameters, objectives, and constraints of the parameter optimization problem of concentric tube continuum robots, 2) the selection of an appropriate optimization algorithm that can handle the multidimensional search space and diversity of the optimization problem with multiple objectives, and 3) the evaluation of the proposed optimization methodology and structural synthesis based on three real applications