La visita a campo como metodología para el proceso de enseñanza-aprendizaje

Dentro de la enseñanza de la geotecnia los viajes a campo son una herramienta útil para superar las limitaciones asociadas a la enseñanza en el aula así como para promover el autoaprendizaje del alumno, el cual se enfrenta en primera persona a la información en estado bruto. Mediante esta comunicación compartimos la experiencia de la visita a las obras de construcción de los Túneles de Sorbas y El Almendral dentro del Máster de "Geología Aplicada a la Obra Civil y los Recursos Hídricos" ofertado por la Universidad de Granada, comentando, con un enfoque docente, la planificación de la actividad en función de los resultados de aprendizaje deseados. Fieldtrips are a good tool to overcome the inherent difficulties associated to teaching engineering geology at the classroom and to encourage student self-learning, when they face raw data. In this paper, we share our recent experience with the organization of a fieldtrip to two tunneling construction site (Sorbas Tunnel and El Almendral Tunnel) for the MSc program of “Applied Geology in Civil Engineering and Water Resources” offered by the University of Granada, discussing, with a educational point of view, the planning and learning outcomes.

A novel method for radio propagation simulation based on automatic 3D environment reconstruction

In this paper, a novel method to simulate radio propagation is presented. The method consists of two steps: automatic 3D scenario reconstruction and propagation modeling. For 3D reconstruction, a machine learning algorithm is adopted and improved to automatically recognize objects in pictures taken from target region, and 3D models are generated based on the recognized objects. The propagation model employs a ray tracing algorithm to compute signal strength for each point on the constructed 3D map. By comparing with other methods, the work presented in this paper makes contributions on reducing human efforts and cost in constructing 3D scene; moreover, the developed propagation model proves its potential in both accuracy and efficiency.

Cooperative off-policy prediction of markov decision processes in adaptive networks

We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in large state spaces. We provide a mean-square-error performance analysis under constant step-sizes. The gain of cooperation in the form of more stability and less bias and variance in the prediction error, is illustrated in the context of a classical model. We show that the improvement in performance is especially significant when the behavior policy of the agents is different from the target policy under evaluation.

Desarrollo de herramientas de autoevaluación de prácticas de procesado digital de la señal.

El objetivo de este proyecto es desarrollar un conjunto de herramientas de auto aprendizaje y autoevaluación del laboratorio de la asignatura "Procesado Digital de la Señal", perteneciente al plan de grado de la Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación de la Universidad Politécnica de Madrid. Con ello se pretende de mejorar el rendimiento académico de los alumnos en dicha asignatura y en la materia "Señales y Sistemas" en general. Para la realización de las prácticas se emplea Matlab, de modo que es necesario integrar esta herramienta en el laboratorio con MOODLE, plataforma de e-learning utilizada para la gestión de las asignaturas a nivel docente, para proporcionar material de estudio y programar actividades de aprendizaje y evaluación. Será fundamental el análisis de la integración de Matlab con MOODLE, de modo que en función de los resultados de los alumnos, se les propongan repeticiones de apartados erróneos, revisiones de resultados y otros aspectos, como autoaprendizaje y autoevaluación que permitan la obtención de las competencias y alcanzar los resultados de aprendizaje, y a los profesores que imparten la asignatura, como herramienta para detectar las deficiencias más significativas en la programación y en las metodologías empleadas en la asignatura para corregir las carencias de los alumnos. ABSTRACT: The aim of this project will be the development of self-learning and self- assessment lab tools for the course "Procesado Digital de la Señal" in order to improve student’s performance in that subject and in the matter "Señales y Sistemas " for grades taught at the Escuela Universitaria de Ingeniería Técnica de Telecomunicación of the Universidad Politécnica de Madrid today. Matlab is used to perform laboratory practices of "Procesado Digital de la Señal “. Matlab is a numerical calculation program. A very powerful tool with a great mathematical processing performance level, so it is necessary to integrate this tool in the laboratory with MOODLE, the current e-learning platform used at the Universidad Politécnica de Madrid for the management of teaching subjects to provide material and to program learning and assessment activities for students. It is therefore essential the analysis of the Matlab integration with Moodle. Thus, depending on the results and grades that students get along the way in the various activities evaluators should conduct, they propose, for example, repetitions of erroneous exercises, reviews of some results and other aspects such as self-learning and self-assessment. This would allow students to obtain the skills and learning to achieve the results set as a target. For teachers who teach the subject will also be a preview of the notes as these tools will be used to identify the most significant shortcomings both in programming and in the methodologies used in "Procesado Digital de la Señal " to act accordingly and correcting shortcomings of the enrolled students.

Detección de intrusiones basada en análisis de eventos del DNS

En este proyecto se hace un análisis en profundidad de las técnicas de ataque a las redes de ordenadores conocidas como APTs (Advanced Persistent Threats), viendo cuál es el impacto que pueden llegar a tener en los equipos de una empresa y el posible robo de información y pérdida monetaria que puede llevar asociada. Para hacer esta introspección veremos qué técnicas utilizan los atacantes para introducir el malware en la red y también cómo dicho malware escala privilegios, obtiene información privilegiada y se mantiene oculto. Además, y cómo parte experimental de este proyecto se ha desarrollado una plataforma para la detección de malware de una red en base a las webs, URLs e IPs que visitan los nodos que la componen. Obtendremos esta visión gracias a la extracción de los logs y registros de DNS de consulta de la compañía, sobre los que realizaremos un análisis exhaustivo. Para poder inferir correctamente qué equipos están infectados o no se ha utilizado un algoritmo de desarrollo propio inspirado en la técnica Belief Propagation (“Propagación basada en creencia”) que ya ha sido usada antes por desarrolladores cómo los de los Álamos en Nuevo México (Estados Unidos) para fines similares a los que aquí se muestran. Además, para mejorar la velocidad de inferencia y el rendimiento del sistema se propone un algoritmo adaptado a la plataforma Hadoop de Apache, por lo que se modifica el paradigma de programación habitual y se busca un nuevo paradigma conocido como MapReduce que consiste en la división de la información en conceptos clave-valor. Por una parte, los algoritmos que existen basados en Belief Propagation para el descubrimiento de malware son propietarios y no han sido publicados completamente hasta la fecha, por otra parte, estos algoritmos aún no han sido adaptados a Hadoop ni a ningún modelo de programación distribuida aspecto que se abordará en este proyecto. No es propósito de este proyecto desarrollar una plataforma comercial o funcionalmente completa, sino estudiar el problema de las APTs y una implementación que demuestre que la plataforma mencionada es factible de implementar. Este proyecto abre, a su vez, un horizonte nuevo de investigación en el campo de la adaptación al modelo MapReduce de algoritmos del tipo Belief Propagation basados en la detección del malware mediante registros DNS. ABSTRACT. This project makes an in-depth investigation about problems related to APT in computer networks nowadays, seeing how much damage could they inflict on the hosts of a Company and how much monetary and information loss may they cause. In our investigation we will find what techniques are generally applied by attackers to inject malware into networks and how this malware escalates its privileges, extracts privileged information and stays hidden. As the main part of this Project, this paper shows how to develop and configure a platform that could detect malware from URLs and IPs visited by the hosts of the network. This information can be extracted from the logs and DNS query records of the Company, on which we will make an analysis in depth. A self-developed algorithm inspired on Belief Propagation technique has been used to infer which hosts are infected and which are not. This technique has been used before by developers of Los Alamos Lab (New Mexico, USA) for similar purposes. Moreover, this project proposes an algorithm adapted to Apache Hadoop Platform in order to improve the inference speed and system performance. This platform replaces the traditional coding paradigm by a new paradigm called MapReduce which splits and shares information among hosts and uses key-value tokens. On the one hand, existing algorithms based on Belief Propagation are part of owner software and they have not been published yet because they have been patented due to the huge economic benefits they could give. On the other hand these algorithms have neither been adapted to Hadoop nor to other distributed coding paradigms. This situation turn the challenge into a complicated problem and could lead to a dramatic increase of its installation difficulty on a client corporation. The purpose of this Project is to develop a complete and 100% functional brand platform. Herein, show a short summary of the APT problem will be presented and make an effort will be made to demonstrate the viability of an APT discovering platform. At the same time, this project opens up new horizons of investigation about adapting Belief Propagation algorithms to the MapReduce model and about malware detection with DNS records.

CAS: A tool for improving autonomous work

The EHEA proposes a student-centered teaching model. Therefore, it seems necessary to actively involve the students in the teaching-learning process. Increasing the active participation of the students is not always easy in mathematical topics, since, when the students just enter the University, their ability to carry out autonomous mathematical work is scarce. In this paper we present some experiences related with the use of Computer Algebra Systems (CAS). All the experiences are designed in order to develop some mathematical competencies and mainly self-learning, the use of technology and team-work. The experiences include some teachers? proposals including: small projects to be executed in small groups, participation in competitions, the design of different CAS-Toolboxes, etc. The results obtained in the experiences, carried out with different groups of students from different engineering studies at different universities, makes us slightly optimistic about the educational value of the model.

A swarm intelligence approach based on coupled oscillators: an application in demand side management with photovoltaic distributed generation

Esta Tesis aborda los problemas de eficiencia de las redes eléctrica desde el punto de vista del consumo. En particular, dicha eficiencia es mejorada mediante el suavizado de la curva de consumo agregado. Este objetivo de suavizado de consumo implica dos grandes mejoras en el uso de las redes eléctricas: i) a corto plazo, un mejor uso de la infraestructura existente y ii) a largo plazo, la reducción de la infraestructura necesaria para suplir las mismas necesidades energéticas. Además, esta Tesis se enfrenta a un nuevo paradigma energético, donde la presencia de generación distribuida está muy extendida en las redes eléctricas, en particular, la generación fotovoltaica (FV). Este tipo de fuente energética afecta al funcionamiento de la red, incrementando su variabilidad. Esto implica que altas tasas de penetración de electricidad de origen fotovoltaico es perjudicial para la estabilidad de la red eléctrica. Esta Tesis trata de suavizar la curva de consumo agregado considerando esta fuente energética. Por lo tanto, no sólo se mejora la eficiencia de la red eléctrica, sino que también puede ser aumentada la penetración de electricidad de origen fotovoltaico en la red. Esta propuesta conlleva grandes beneficios en los campos económicos, social y ambiental. Las acciones que influyen en el modo en que los consumidores hacen uso de la electricidad con el objetivo producir un ahorro energético o un aumento de eficiencia son llamadas Gestión de la Demanda Eléctrica (GDE). Esta Tesis propone dos algoritmos de GDE diferentes para cumplir con el objetivo de suavizado de la curva de consumo agregado. La diferencia entre ambos algoritmos de GDE reside en el marco en el cual estos tienen lugar: el marco local y el marco de red. Dependiendo de este marco de GDE, el objetivo energético y la forma en la que se alcanza este objetivo son diferentes. En el marco local, el algoritmo de GDE sólo usa información local. Este no tiene en cuenta a otros consumidores o a la curva de consumo agregado de la red eléctrica. Aunque esta afirmación pueda diferir de la definición general de GDE, esta vuelve a tomar sentido en instalaciones locales equipadas con Recursos Energéticos Distribuidos (REDs). En este caso, la GDE está enfocada en la maximización del uso de la energía local, reduciéndose la dependencia con la red. El algoritmo de GDE propuesto mejora significativamente el auto-consumo del generador FV local. Experimentos simulados y reales muestran que el auto-consumo es una importante estrategia de gestión energética, reduciendo el transporte de electricidad y alentando al usuario a controlar su comportamiento energético. Sin embargo, a pesar de todas las ventajas del aumento de auto-consumo, éstas no contribuyen al suavizado del consumo agregado. Se han estudiado los efectos de las instalaciones locales en la red eléctrica cuando el algoritmo de GDE está enfocado en el aumento del auto-consumo. Este enfoque puede tener efectos no deseados, incrementando la variabilidad en el consumo agregado en vez de reducirlo. Este efecto se produce porque el algoritmo de GDE sólo considera variables locales en el marco local. Los resultados sugieren que se requiere una coordinación entre las instalaciones. A través de esta coordinación, el consumo debe ser modificado teniendo en cuenta otros elementos de la red y buscando el suavizado del consumo agregado. En el marco de la red, el algoritmo de GDE tiene en cuenta tanto información local como de la red eléctrica. En esta Tesis se ha desarrollado un algoritmo autoorganizado para controlar el consumo de la red eléctrica de manera distribuida. El objetivo de este algoritmo es el suavizado del consumo agregado, como en las implementaciones clásicas de GDE. El enfoque distribuido significa que la GDE se realiza desde el lado de los consumidores sin seguir órdenes directas emitidas por una entidad central. Por lo tanto, esta Tesis propone una estructura de gestión paralela en lugar de una jerárquica como en las redes eléctricas clásicas. Esto implica que se requiere un mecanismo de coordinación entre instalaciones. Esta Tesis pretende minimizar la cantidad de información necesaria para esta coordinación. Para lograr este objetivo, se han utilizado dos técnicas de coordinación colectiva: osciladores acoplados e inteligencia de enjambre. La combinación de estas técnicas para llevar a cabo la coordinación de un sistema con las características de la red eléctrica es en sí mismo un enfoque novedoso. Por lo tanto, este objetivo de coordinación no es sólo una contribución en el campo de la gestión energética, sino también en el campo de los sistemas colectivos. Los resultados muestran que el algoritmo de GDE propuesto reduce la diferencia entre máximos y mínimos de la red eléctrica en proporción a la cantidad de energía controlada por el algoritmo. Por lo tanto, conforme mayor es la cantidad de energía controlada por el algoritmo, mayor es la mejora de eficiencia en la red eléctrica. Además de las ventajas resultantes del suavizado del consumo agregado, otras ventajas surgen de la solución distribuida seguida en esta Tesis. Estas ventajas se resumen en las siguientes características del algoritmo de GDE propuesto: • Robustez: en un sistema centralizado, un fallo o rotura del nodo central provoca un mal funcionamiento de todo el sistema. La gestión de una red desde un punto de vista distribuido implica que no existe un nodo de control central. Un fallo en cualquier instalación no afecta el funcionamiento global de la red. • Privacidad de datos: el uso de una topología distribuida causa de que no hay un nodo central con información sensible de todos los consumidores. Esta Tesis va más allá y el algoritmo propuesto de GDE no utiliza información específica acerca de los comportamientos de los consumidores, siendo la coordinación entre las instalaciones completamente anónimos. • Escalabilidad: el algoritmo propuesto de GDE opera con cualquier número de instalaciones. Esto implica que se permite la incorporación de nuevas instalaciones sin afectar a su funcionamiento. • Bajo coste: el algoritmo de GDE propuesto se adapta a las redes actuales sin requisitos topológicos. Además, todas las instalaciones calculan su propia gestión con un bajo requerimiento computacional. Por lo tanto, no se requiere un nodo central con un alto poder de cómputo. • Rápido despliegue: las características de escalabilidad y bajo coste de los algoritmos de GDE propuestos permiten una implementación rápida. No se requiere una planificación compleja para el despliegue de este sistema. ABSTRACT This Thesis addresses the efficiency problems of the electrical grids from the consumption point of view. In particular, such efficiency is improved by means of the aggregated consumption smoothing. This objective of consumption smoothing entails two major improvements in the use of electrical grids: i) in the short term, a better use of the existing infrastructure and ii) in long term, the reduction of the required infrastructure to supply the same energy needs. In addition, this Thesis faces a new energy paradigm, where the presence of distributed generation is widespread over the electrical grids, in particular, the Photovoltaic (PV) generation. This kind of energy source affects to the operation of the grid by increasing its variability. This implies that a high penetration rate of photovoltaic electricity is pernicious for the electrical grid stability. This Thesis seeks to smooth the aggregated consumption considering this energy source. Therefore, not only the efficiency of the electrical grid is improved, but also the penetration of photovoltaic electricity into the grid can be increased. This proposal brings great benefits in the economic, social and environmental fields. The actions that influence the way that consumers use electricity in order to achieve energy savings or higher efficiency in energy use are called Demand-Side Management (DSM). This Thesis proposes two different DSM algorithms to meet the aggregated consumption smoothing objective. The difference between both DSM algorithms lie in the framework in which they take place: the local framework and the grid framework. Depending on the DSM framework, the energy goal and the procedure to reach this goal are different. In the local framework, the DSM algorithm only uses local information. It does not take into account other consumers or the aggregated consumption of the electrical grid. Although this statement may differ from the general definition of DSM, it makes sense in local facilities equipped with Distributed Energy Resources (DERs). In this case, the DSM is focused on the maximization of the local energy use, reducing the grid dependence. The proposed DSM algorithm significantly improves the self-consumption of the local PV generator. Simulated and real experiments show that self-consumption serves as an important energy management strategy, reducing the electricity transport and encouraging the user to control his energy behavior. However, despite all the advantages of the self-consumption increase, they do not contribute to the smooth of the aggregated consumption. The effects of the local facilities on the electrical grid are studied when the DSM algorithm is focused on self-consumption maximization. This approach may have undesirable effects, increasing the variability in the aggregated consumption instead of reducing it. This effect occurs because the algorithm only considers local variables in the local framework. The results suggest that coordination between these facilities is required. Through this coordination, the consumption should be modified by taking into account other elements of the grid and seeking for an aggregated consumption smoothing. In the grid framework, the DSM algorithm takes into account both local and grid information. This Thesis develops a self-organized algorithm to manage the consumption of an electrical grid in a distributed way. The goal of this algorithm is the aggregated consumption smoothing, as the classical DSM implementations. The distributed approach means that the DSM is performed from the consumers side without following direct commands issued by a central entity. Therefore, this Thesis proposes a parallel management structure rather than a hierarchical one as in the classical electrical grids. This implies that a coordination mechanism between facilities is required. This Thesis seeks for minimizing the amount of information necessary for this coordination. To achieve this objective, two collective coordination techniques have been used: coupled oscillators and swarm intelligence. The combination of these techniques to perform the coordination of a system with the characteristics of the electric grid is itself a novel approach. Therefore, this coordination objective is not only a contribution in the energy management field, but in the collective systems too. Results show that the proposed DSM algorithm reduces the difference between the maximums and minimums of the electrical grid proportionally to the amount of energy controlled by the system. Thus, the greater the amount of energy controlled by the algorithm, the greater the improvement of the efficiency of the electrical grid. In addition to the advantages resulting from the smoothing of the aggregated consumption, other advantages arise from the distributed approach followed in this Thesis. These advantages are summarized in the following features of the proposed DSM algorithm: • Robustness: in a centralized system, a failure or breakage of the central node causes a malfunction of the whole system. The management of a grid from a distributed point of view implies that there is not a central control node. A failure in any facility does not affect the overall operation of the grid. • Data privacy: the use of a distributed topology causes that there is not a central node with sensitive information of all consumers. This Thesis goes a step further and the proposed DSM algorithm does not use specific information about the consumer behaviors, being the coordination between facilities completely anonymous. • Scalability: the proposed DSM algorithm operates with any number of facilities. This implies that it allows the incorporation of new facilities without affecting its operation. • Low cost: the proposed DSM algorithm adapts to the current grids without any topological requirements. In addition, every facility calculates its own management with low computational requirements. Thus, a central computational node with a high computational power is not required. • Quick deployment: the scalability and low cost features of the proposed DSM algorithms allow a quick deployment. A complex schedule of the deployment of this system is not required.

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

Experiences with free and open courses using on-line multimedia resources

This paper analyzes the learning experiences and opinions obtained from a group of undergraduate students in their interaction with several on-line multimedia resources included in a free on-line course about Computer Networks. These new educational resources employed are based on the Web2.0 approach such as blogs, videos and virtual labs which have been added in a web-site for distance self-learning.

Open educational resources: the role of OCW, blogs and videos in computer network classrooms

This paper analyzes the learning experiences and opinions obtained from a group of undergraduate students in their interaction with several on-line multimedia resources included in a free on-line course about Computer Networks. These new educational resources employed are based on the Web 2.0 approach such as blogs, videos and virtual labs which have been added in a web-site for distance self-learning.

Practical experiences on a real pumping system emulated by a hardware model and used as a remote laboratory

Paper submitted to ACE 2013, 10th IFAC Symposium on Advances in Control Education, University of Sheffield, UK, August 28-30, 2013.

New Approaches for Teaching Soil and Rock Mechanics Using Information and Communication Technologies

Soil and rock mechanics are disciplines with a strong conceptual and methodological basis. Initially, when engineering students study these subjects, they have to understand new theoretical phenomena, which are explained through mathematical and/or physical laws (e.g. consolidation process, water flow through a porous media). In addition to the study of these phenomena, students have to learn how to carry out estimations of soil and rock parameters in laboratories according to standard tests. Nowadays, information and communication technologies (ICTs) provide a unique opportunity to improve the learning process of students studying the aforementioned subjects. In this paper, we describe our experience of the incorporation of ICTs into the classical teaching-learning process of soil and rock mechanics and explain in detail how we have successfully developed various initiatives which, in summary, are: (a) implementation of an online social networking and microblogging service (using Twitter) for gradually sending key concepts to students throughout the semester (gradual learning); (b) detailed online virtual laboratory tests for a delocalized development of lab practices (self-learning); (c) integration of different complementary learning resources (e.g. videos, free software, technical regulations, etc.) using an open webpage. The complementary use to the classical teaching-learning process of these ICT resources has been highly satisfactory for students, who have positively evaluated this new approach.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.

Description and Prediction of Freezing of Gait in Parkinson's Disease from Wearable Inertial Measurements Units

Parkinson's disease (PD) is a neuro-degenerative disorder, the second most common after Alzheimer's disease. After diagnosis, treatments can help to relieve the symptoms, but there is no known cure for PD. PD is characterized by a combination of motor and no-motor dysfunctions. Among the motor symptoms there is the so called Freezing of Gait (FoG). The FoG is a phenomenon in PD patients in which the feet stock to the floor and is difficult for the patient to initiate movement. FoG is a severe problem, since it is associated with falls, anxiety, loss of mobility, accidents, mortality and it has substantial clinical and social consequences decreasing the quality of life in PD patients. Medicine can be very successful in controlling movements disorders and dealing with some of the PD symptoms. However, the relationship between medication and the development of FoG remains unclear. Several studies have demonstrated that visual or auditory rhythmical cuing allows PD patients to improve their motor abilities. Rhythmic auditory stimulation (RAS) was shown to be particularly effective at improving gait, specially with patients that manifest FoG. While RAS allows to reduce the time and the effects of FoGs occurrence in PD patients after the FoG is detected, it can not avoid the episode due to the latency of detection. An improvement of the system would be the prediction of the FoG. This thesis was developed following two main objectives: (1) the finding of specifics properties during pre FoG periods different from normal walking context and other walking events like turns and stops using the information provided by the inertial measurements units (IMUs) and (2) the formulation of a model for automatically detect the pre FoG patterns in order to completely avoid the upcoming freezing event in PD patients. The first part focuses on the analysis of different methods for feature extraction which might lead in the FoG occurrence.

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.