Self-adaptation of Genetic Operators Through Genetic Programming Techniques

Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.Comment: Presented in GECCO 201

{Uso de Redes Neuronales en Computación Evolutiva para solucionar problemas de Caja Negra

Uso de Algoritmos Genéticos en la busqueda en el espacio desoluciones se optimiza el uso de Redes Neuronales en la busqueda de modelossustitutos para solucionar problemas de Caja Negra

Toward Automating EA Configuration: The Parent Selection Stage

One of the obstacles to Evolutionary Algorithms (EAs) fulfilling their promise as easy to use general-purpose problem solvers, is the difficulty of correctly configuring them for specific problems such as to obtain satisfactory performance. Having a mechanism for automatically configuring parameters and operators of every stage of the evolutionary life-cycle would give EAs a more widely spread popularity in the non-expert community. This paper investigates automatic configuration of one of the stages of the evolutionary life-cycle, the parent selection, via a new concept of semi-autonomous parent selection, where mate selection operators are encoded and evolved as in Genetic Programming. We compare the performance of the EA with semi-autonomous parent selection to that of a manually configured EA on three common test problems to determine the “price” we pay for user-friendliness

Robust evolutionary algorithms

Evolutionary Algorithms (EAs) have shown great potential to solve complex real world problems, but their dependence on problem specific configuration in order to obtain high quality performance prevents EAs from achieving widespread use. While it is widely accepted that statically configuring an EA is already a complex problem, dynamic configuration of an EA is a combinatorially harder problem. Evidence provided here supports the claim that EAs achieve the best results when using dynamic configurations. By designing methods that automatically configure parts of an EA or by changing how EAs work to avoid configurable aspects, EAs can be made more robust, allowing them better performance on a wider variety of problems with less requirements on the user. Two methods are presented in this thesis to increase the robustness of EAs. The first is a novel algorithm designed to automatically configure and dynamically update the recombination method which is used by the EA to exploit known information to create new solutions. The techniques used by this algorithm can likely be applied to other aspects of an EA in the future, leading to even more robust EAs. The second is an existing set of algorithms which only require a single configurable parameter. The analysis of the existing set led to the creation of a new variation, as well as a better understanding of how these algorithms work. Both methods are able to outperform more traditional EAs while also making both easier to apply to new problems. By building upon these methods, and perhaps combining them, EAs can become even more robust and become more widely used --Abstract, page iv

Data Stream Mining: an Evolutionary Approach

Este trabajo presenta un algoritmo para agrupar flujos de datos, llamado ESCALIER. Este algoritmo es una extensión del algoritmo de agrupamiento evolutivo ECSAGO Evolutionary Clustering with Self Adaptive Genetic Operators. ESCALIER toma el proceso evolutivo propuesto por ECSAGO para encontrar grupos en los flujos de datos, los cuales son definidos por la técnica Sliding Window. Para el mantenimiento y olvido de los grupos detectados a través de la evolución de los datos, ESCALIER incluye un mecanismo de memoria inspirado en la teoría de redes inmunológicas artificiales. Para probar la efectividad del algoritmo, se realizaron experimentos utilizando datos sintéticos simulando un ambiente de flujos de datos, y un conjunto de datos reales.Abstract. This work presents a data stream clustering algorithm called ESCALIER. This algorithm is an extension of the evolutionary clustering ECSAGO - Evolutionary Clustering with Self Adaptive Genetic Operators. ESCALIER takes the advantage of the evolutionary process proposed by ECSAGO to find the clusters in the data streams. They are defined by sliding window technique. To maintain and forget clusters through the evolution of the data, ESCALIER includes a memory mechanism inspired by the artificial immune network theory. To test the performance of the algorithm, experiments using synthetic data, simulating the data stream environment, and a real dataset are carried out.Maestrí

Goal-Based Control and Planning in Biped Locomotion Using Computational Intelligence Methods

Este trabajo explora la aplicación de campos neuronales, a tareas de control dinámico en el domino de caminata bípeda. En una primera aproximación, se propone una arquitectura de control que usa campos neuronales en 1D. Esta arquitectura de control es evaluada en el problema de estabilidad para el péndulo invertido de carro y barra, usado como modelo simplificado de caminata bípeda. El controlador por campos neuronales, parametrizado tanto manualmente como usando un algoritmo evolutivo (EA), se compara con una arquitectura de control basada en redes neuronales recurrentes (RNN), también parametrizada por por un EA. El controlador por campos neuronales parametrizado por EA se desempeña mejor que el parametrizado manualmente, y es capaz de recuperarse rápidamente de las condiciones iniciales más problemáticas. Luego, se desarrolla una arquitectura extendida de control y planificación usando campos neurales en 2D, y se aplica al problema caminata bípeda simple (SBW). Para ello se usa un conjunto de valores _óptimos para el parámetro de control, encontrado previamente usando algoritmos evolutivos. El controlador óptimo por campos neuronales obtenido se compara con el controlador lineal propuesto por Wisse et al., y a un controlador _optimo tabular que usa los mismos parámetros óptimos. Si bien los controladores propuestos para el problema SBW implementan una estrategia activa de control, se aproximan de manera más cercana a la caminata dinámica pasiva (PDW) que trabajos previos, disminuyendo la acción de control acumulada. / Abstract. This work explores the application of neural fields to dynamical control tasks in the domain of biped walking. In a first approximation, a controller architecture that uses 1D neural fields is proposed. This controller architecture is evaluated using the stability problem for the cart-and-pole inverted pendulum, as a simplified biped walking model. The neural field controller is compared, parameterized both manually and using an evolutionary algorithm (EA), to a controller architecture based on a recurrent neural neuron (RNN), also parametrized by an EA. The non-evolved neural field controller performs better than the RNN controller. Also, the evolved neural field controller performs better than the non-evolved one and is able to recover fast from worst-case initial conditions. Then, an extended control and planning architecture using 2D neural fields is developed and applied to the SBW problem. A set of optimal parameter values, previously found using an EA, is used as parameters for neural field controller. The optimal neural field controller is compared to the linear controller proposed by Wisse et al., and to a table-lookup controller using the same optimal parameters. While being an active control strategy, the controllers proposed here for the SBW problem approach more closely Passive Dynamic Walking (PDW) than previous works, by diminishing the cumulative control action.Maestrí

Topological and algebraic characterization of coverings sets obtained in rough sets discretization and attribute reduction algorithms

Abstract. A systematic study on approximation operators in covering based rough sets and some relations with relation based rough sets are presented. Two different frameworks of approximation operators in covering based rough sets were unified in a general framework of dual pairs. This work establishes some relationships between the most important generalization of rough set theory: Covering based and relation based rough sets. A structured genetic algorithm to discretize, to find reducts and to select approximation operators for classification problems is presented.Se presenta un estudio sistemático de los diferentes operadores de aproximación en conjuntos aproximados basados en cubrimientos y operadores de aproximación basados en relaciones binarias. Se unifican dos marcos de referencia sobre operadores de aproximación basados en cubrimientos en un único marco de referencia con pares duales. Se establecen algunas relaciones entre operadores de aproximación de dos de las más importantes generalizaciones de la teoría de conjuntos aproximados. Finalmente, se presenta un algoritmo genético estructurado, para discretizar, reducir atributos y seleccionar operadores de aproximación, en problemas de clasificación.Doctorad

Estudio de factibilidad para el aprovechamiento de ACUs en la ciudad de Bogotá

El crecimiento de la población a nivel mundial conlleva varios retos, especialmente los correspondientes a la correcta gestión de residuos, y principalmente en áreas de gran concentración poblacional. Sin las adecuadas prácticas de disposición, los residuos pueden causar un gran número de problemas a nivel ambiental, económico y social. Entre los residuos más problemáticos en términos de manejo y disposición, el Aceite de Cocina Usado (ACU) es un residuo alimenticio que se produce en grandes volúmenes. El manejo inapropiado del ACU genera una gran variedad de inconvenientes tales como daños a la infraestructura, inundaciones, proliferación de pestes, polución en los ecosistemas e incluso problemas de salud pública por recolección y redistribución ilegal del residuo. Particularmente en Bogotá, la ciudad capital de Colombia, con aproximadamente 10 millones de habitantes en su área metropolitana, los ACU representan una gran problemática. En este aspecto, este trabajo se enfoca en el estudio, caracterización y optimización de la cadena de recolección del ACU en la ciudad de Bogotá para su posterior empleo como materia prima oleoquímica. Primero, se realizó la caracterización de la cadena logística del ACU en la ciudad de Bogotá. Esta tarea involucró la ubicación y naturaleza de los generadores, volúmenes disponibles, características del ACU, caracterización de las prácticas de recolección y rutas, y el inventario de consumo de recursos durante el proceso de recolección. Luego, se construyó e implementó un modelo computacional del esquema de recolección en software libre (Python). Para esto se utilizó una aproximación de problema de enrutamiento de vehículos empleando caminos hamiltonianos ponderados. Los pesos fueron definidos de acuerdo con indicadores económicos y ambientales calculados como costos en dinero y emisiones de CO2 equivalente, respectivamente. Empleando un algoritmo genético se logró identificar el conjunto de rutas de recolección que minimizan los costos y las emisiones de CO2, y la localización de un punto de acopio que minimizaría los costos de la cadena de recolección. Finalmente, se hizo una identificación preliminar de los derivados oleoquímicos con potencial para ser obtenidos a partir de los ACUs recolectados.Global population growth involves a variety of challenges, especially those related to the correct management of waste, mainly in highly populated areas. Without the correct disposal practices, waste can cause a large number of environmental, economic and social problems. Among the most problematic wastes in terms of handling and disposal, Used Cooking Oil (ACU) standouts as a food waste that is produced in large volumes. The inadequate management of the ACU generates a wide variety of problems such as damage to infrastructure, floods, pest proliferation, pollution of ecosystems and even public health problems due to the illegal collection and redistribution of ACU as new edible oil. Particularly in Bogotá, the capital city of Colombia, with approximately 10 million inhabitants in its metropolitan area, the ACU represent a major problem. In this aspect, this work focuses on the study, characterization and optimization of the ACU collection chain in the city of Bogotá for its subsequent use as oleochemical raw material. First, the characterization of the ACU logistics chain in the city of Bogotá was carried out. This task involved the location and identification of ACUs generators, available volumes, characteristics of the ACU, characterization of collection practices and routes, and the inventory of resource consumption during the collection process. Then, a computational model of the supply chain was constructed in an open-source software (Python). For this, a vehicle routing problem approximation was used using weighted Hamiltonian roads. Weights were defined according to economic and environmental indicators calculated as costs and equivalent CO2 emissions, respectively. By the use of a genetic algorithm, it was possible to identify the set of collection routes that minimize costs and CO2 emissions, and the location of a collection point that would minimize the costs of the collection chain. Finally, a preliminary identification of the oleochemical derivatives with potential to be obtained from the collected ACUs was made.Valorization of Urban Used Cooking Oils by transformation into value added oleochemicals. Study case for Bogota, ColombiaLínea de Investigación: Biorrefinerías – biocombustiblesMaestrí