Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

Machine Learning is concerned in constructing models which can learn and make predictions based on data. Rule extraction from real world data that are usually tainted with noise, ambiguity, and uncertainty, automatically requires feature selection. Neuro-Fuzzy system (NFS) which is known with its prediction performance has the difficulty in determining the proper number of rules and the number of membership functions for each rule. An enhanced hybrid Genetic Algorithm based Fuzzy Bayesian classifier (GA-FBC) was proposed to help the NFS in the rule extraction. Feature selection was performed in the rule level overcoming the problems of the FBC which depends on the frequency of the features leading to ignore the patterns of small classes. As dealing with a real world problem such as the Air/Fuel Ratio (AFR) prediction, a multi-objective problem is adopted. The GA-FBC uses mutual information entropy, which considers the relevance between feature attributes and class attributes. A fitness function is proposed to deal with multi-objective problem without weight using a new composition method. The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. Predictive accuracy and the complexity of the Fuzzy Rule Base System (FRBS) including number of rules and number of terms in each rule were taken as terms of evaluation. It was also compared to the original GA-FBC depending on the frequency not on Mutual Information (MI). Experimental results using Air/Fuel Ratio (AFR) data sets show that the new model participates in decreasing the average number of attributes in the rule and sometimes in increasing the average performance compared to other models. This work facilitates in achieving a self-generating FRBS from real data. The GA-FBC can be used as a new direction in machine learning research. This research contributes in controlling automobile emissions in helping the reduction of one of the most causes of pollution to produce greener environment

Basic Evolutionary Approach to the Traveling Salesman Problem

Evolutionary programming (EP) is a metaheuristic method developed as an alternative approach to artificial intelligence. The aim of this paper is to bring an introduction to EP algorithms through the implementation of the basic D. B. Fogel’s Evolutionary Programing approach of 1988 and the emulation of his results in order to analyze the performance of the evolutionary programming method on solving a benchmark test case. The EP approach is implemented thru a simple simulation of natural evolution and the allowance of probabilistic survival of individuals. The novelty of this paper relies on testing the algorithm performance in some problems of well-known benchmark instances of the Traveling Salesman Problem, where that 1988 evolutionary approach was not tested. The reproduction of 1988 D. B. Fogel’s approach was possible, the found average error of this method for 200000 offspring applied to the benchmark instances was found to be in the order of the 10%

Neuro-evolution search methodologies for collective self-driving vehicles

Recently there has been an increasing amount of research into autonomous vehicles for real-world driving. Much progress has been made in the past decade with many automotive manufacturers demonstrating real-world prototypes. Current predictions indicate that roads designed exclusively for autonomous vehicles will be constructed and thus this thesis explores the use of methods to automatically produce controllers for autonomous vehicles that must navigate with each other on these roads. Neuro-Evolution, a method that combines evolutionary algorithms with neural networks, has shown to be effective in reinforcement-learning, multi-agent tasks such as maze navigation, biped locomotion, autonomous racing vehicles and fin-less rocket control. Hence, a neuro-evolution method is selected and investigated for the controller evolution of collective autonomous vehicles in homogeneous teams. The impact of objective and non-objective search (and a combination of both, a hybrid method) for controller evolution is comparatively evaluated for robustness on a range of driving tasks and collection sizes. Results indicate that the objective search was able to generalise the best on unseen task environments compared to all other methods and the hybrid approach was able to yield desired task performance on evolution far earlier than both approaches but was unable to generalise as effectively over new environments

Análisis y prototipado de un algoritmo genético modificado para solucionar el problema de ruteo de vehículos con ventanas de tiempo (VPTWR), prioridad de metas económicas y componente medio ambiental

El origen de los problemas de ruteo se da en el siglo XVIII cuando habitantes de Königsberg, un pueblo de Rusia, debate sobre cuál es la ruta que pasa una sola vez por los siete puentes que atravesaban el río Pregel regresando al punto de origen; este problema lo propuso el matemático suizo Leonhard Euler, quien en el año 1736 demostró que no existía ninguna, además de hacer solo referencia a la existencia de un camino y no a la búsqueda del óptimo, lo que conduce al mismo planteamiento de los problemas de rutas; uno de los más conocidos a lo largo de la historia es el problema de rutas de vehículo (VRP) que ha sido de gran importancia e influencia en investigaciones y estudios enfocados en implementar algoritmos que permitan encontrar una solución óptima. Este tipo de problemas son considerados difíciles de resolver y dentro de la optimización combinatoria son conocidos como problemas NP-Hard, dado que no se obtiene una solución de manera eficiente; así mismo dentro de la teoría de la complejidad computacional pertenecen a la clase NP-Completos, al no poderse garantizar hallar la mejor solución en un tiempo de cómputo razonable, ya que este aumenta de manera exponencial, generando así una búsqueda de soluciones aproximadas, siendo conveniente emplear métodos heurísticos y metaheurísticos que aplican el conocimiento del problema para acercarse a la solución en un tiempo de computo razonable..

Identificación de arquitecturas software basadas en componentes mediante Programación Evolutiva

Premio extraordinario de Trabajo Fin de Máster curso 2012-2013.Sistemas InteligentesLa construcción de sistemas software de calidad constituye uno de los principales retos a los que se enfrentan los ingenieros informáticos en la actualidad, pues deben cumplir las expectativas marcadas por los destinatarios ajustándose al tiempo y coste planificado. La Ingeniería del Software, como método sistemático para el desarrollo del software, facilita esta labor permitiendo reducir fallos y fomentando su reutilización. El análisis arquitéctonico constituye una fase muy importante del diseño del software, pues en él se identifican las funcionalidades del mismo, así como sus relaciones, permitiendo obtener una visión global del sistema en una fase temprana de su desarrollo. En este contexto, donde la experiencia del arquitecto es determinante, la obtención de métodos y herramientas semiautomáticos que apoyen en la toma de decisiones de diseño abre un nuevo marco para la aplicación de técnicas de Inteligencia Artificial. Este trabajo presenta un modelo de identificación de arquitecturas basadas en componentes mediante un algoritmo de Programación Evolutiva (EP), que simula la abstracción de modelos arquitectónicos a partir de otro tipo de información de análisis, como la presente en los diagramas de clases. Para ello se ha abordado la representación, evaluación y manejo de soluciones para ser procesadas adecuadamente por un algoritmo evolutivo. Los resultados obtenidos reflejan la posibilidad de “evolucionar” arquitecturas software para encontrar aquellas que mejor cumplen los criterios de diseño requeridos por los expertos

Aplicação do Algoritmo Genético em um Problema de Engenharia Logística

The research work described in this paper main aim to investigate the effectiveness of the Genetic Algorithm applied in solving a problem of Logistics Engineering. In addition, the paper presents a dense theory on the subject in order to contribute to researchers in this area. With this clear objectives, the composition of this paper will initially clarify the technical concepts used. Subsequently, the problem that will be solved as well as its modeling is presented, so that at the end an algorithm is presented, focusing on the construction of the logic of the algorithm, as well as the data obtained that prove the effectiveness tool as a way of solving the defined problem. The concepts behind the algorithm used here, are derived from the most recent studies on Artificial Intelligence and are based on biological studies of the theory of evolution and genetics.El trabajo de investigación descrito en este artículo tiene como objetivo principal investigar la efectividad del Algoritmo Genético aplicado en la resolución de un problema de Ingeniería Logística. Además, el artículo presenta una teoría densa sobre el tema con el fin de contribuir a los investigadores en esta área. Con estos objetivos claros, la composición de este trabajo permitirá aclarar inicialmente los conceptos técnicos utilizados. Posteriormente se presenta el problema que se resolverá así como su modelado, de manera que al final se presenta un algoritmo, enfocándose en la construcción de la lógica del algoritmo, así como los datos obtenidos que comprueban la efectividad de la herramienta como herramienta. forma de resolver el problema definido. Los conceptos detrás del algoritmo utilizado aquí, se derivan de los estudios más recientes sobre Inteligencia Artificial y se basan en estudios biológicos de la teoría de la evolución y la genética.O trabalho de pesquisa descrito neste artigo tem como objetivo principal investigar a eficácia do Algoritmo Genético aplicado na solução de um problema de Engenharia Logística. Além disso, o artigo apresenta uma densa teoria sobre o assunto a fim de contribuir com pesquisadores da área. Com estes objetivos claros, a composição deste artigo irá inicialmente esclarecer os conceitos técnicos utilizados. Posteriormente, é apresentado o problema que será resolvido bem como sua modelagem, de forma que ao final seja apresentado um algoritmo, com foco na construção da lógica do algoritmo, bem como os dados obtidos que comprovam a eficácia da ferramenta como uma forma de resolver o problema definido. Os conceitos por trás do algoritmo aqui utilizado são derivados dos estudos mais recentes em Inteligência Artificial e são baseados em estudos biológicos da teoria da evolução e da genética

Biologically inspired evolutionary temporal neural circuits

Biological neural networks have always motivated creation of new artificial neural networks, and in this case a new autonomous temporal neural network system. Among the more challenging problems of temporal neural networks are the design and incorporation of short and long-term memories as well as the choice of network topology and training mechanism. In general, delayed copies of network signals can form short-term memory (STM), providing a limited temporal history of events similar to FIR filters, whereas the synaptic connection strengths as well as delayed feedback loops (ER circuits) can constitute longer-term memories (LTM). This dissertation introduces a new general evolutionary temporal neural network framework (GETnet) through automatic design of arbitrary neural networks with STM and LTM. GETnet is a step towards realization of general intelligent systems that need minimum or no human intervention and can be applied to a broad range of problems. GETnet utilizes nonlinear moving average/autoregressive nodes and sub-circuits that are trained by enhanced gradient descent and evolutionary search in terms of architecture, synaptic delay, and synaptic weight spaces. The mixture of Lamarckian and Darwinian evolutionary mechanisms facilitates the Baldwin effect and speeds up the hybrid training. The ability to evolve arbitrary adaptive time-delay connections enables GETnet to find novel answers to many classification and system identification tasks expressed in the general form of desired multidimensional input and output signals. Simulations using Mackey-Glass chaotic time series and fingerprint perspiration-induced temporal variations are given to demonstrate the above stated capabilities of GETnet

Aplicação de algoritmo genético em um problema da engenharia logística

The research work described in this document intends to develop a Genetic Algorithm with application bias in solving a Logistic Engineering problem. Having this clear objective the composition of the research will be in the first moment the clarification of the technical concepts used throughout the project. After this, the problem will be solved, as well as its modeling, so that in the end an algorithm is presented, focusing on the construction of the logic of the algorithm instead of the concepts related to the chosen programming language, as well as the obtained data that prove the effectiveness of the tool as a solution to the defined problem. The concepts behind the algorithm used here derive from the most recent studies on Artificial Intelligence and have their basis in the studies of the biology of evolutionary theory and genetics.O trabalho de pesquisa descrito nesse documento tem o intuito de desenvolver um Algoritmo Genético com viés de aplicação na resolução de um problema da Engenharia Logística. Tendo esse objetivo claro a composição da pesquisa será em um primeiro momento o esclarecimento dos conceitos técnicos utilizados ao longo do projeto. Após isso é apresentado o problema que será solucionado assim como sua modelagem, para que ao final seja apresentado um algoritmo, focando na construção da lógica do algoritmo ao invés dos conceitos relativos a linguagem de programação escolhida, assim como os dados obtidos que comprovem a eficácia da ferramenta como forma de solução para o problema definido. Os conceitos por trás do algoritmo aqui utilizado, derivam dos estudos mais recentes sobre Inteligência Artificial e tem sua fundamentação nos estudos da biologia da teoria da evolução e genética

Desarrollo de un algoritmo Ant Colony Optimization para tareas de clustering en Apache Spark

En los últimos años se ha producido un incremento en la cantidad de datos generada por las redes sociales, logs de software, dispositivos móviles y sensores, entre otros. Dicha cantidad de datos es de tal magnitud que se requieren de nuevos paradigmas de computación para el correcto análisis de la información contenida en ellos. En este entorno ha surgido el área de Big Data que se usa para hacer referencia a los desafíos y ventajas derivadas de la recolección y procesado de grandes cantidades de datos [1]. De una manera más formal, el Big Data se define como la cantidad de datos que exceden las capacidades de cómputo de un determinado sistema en términos de consumo de memoria y/o tiempo[2]. La computación distribuida permite contar con múltiples ordenadores interconectados entre sí formando clusters, consiguiendo una capacidad conjunta mayor que con un único ordenador más potente. En la actualidad existen varios frameworks para el análisis de Big Data que han atraído el interés tanto de la comunidad científica como de la industria. El primer framework es Apache Hadoop [3], desarrollado por Google y que se basa en el enfoque de MapReduce[4]. Sin embargo, el nuevo framework Apache Spark[5], desarrollado por la universidad de Berkeley, se está haciendo bastante popular. Hacer un buen uso de estos frameworks requiere adaptar los algoritmos que se quieran usar a las características del sistema sobre el cual se vayan a desplegar, encontrando puntos de paralelización óptimos que aprovechen las fortalezas de dichos frameworks. La correcta adaptación de los algoritmos a la plataforma de Big Data es un aspecto crucial ya que repercutirá en el rendimiento de dicho algoritmo. Este Trabajo de Fin de Máster se centrará en el estudio y desarrollo algoritmo de clusterización de Ant Colony Optimization (ACOC)[6, 7] sobre la plataforma Apache Spark. Para la correcta validación del sistema desarrollado, se realizarán tareas de clustering sobre varios conjuntos de pruebas sencillos. Una vez que el sistema esté validado y si se dispone de tiempo suficiente, se estudiará el rendimiento del sistema ante un problema de Big Data como pueden ser las tareas de clustering sobre datos de redes sociales como Twitter.In recent years there has been an increase in the amount of data generated by social networks, software logs, mobile devices and sensors, among others. This amount of data is such that require new computing paradigms for proper analysis of the information contained therein. In this environment it has emerged Big Data. This term is used to refer to the challenges and bene ts of collecting and processing large amounts of data[1]. In a more formal way, Big Data is de ned as the amount of data that exceed the computing capabilities of a given system in terms of memory consumption and/or time[2]. Distributed computing allows for multiple interconnected computers together to form clusters, achieving a combined capacity greater than a single more powerful computer. At present there are several frameworks for analysis of Big Data that have attracted the interest of both the scienti c community and industry. The rst one is Apache Hadoop [3], developed by Google and based on the MapReduce approach[4]. However, the new framework Apache Spark[5], developed by the University of Berkeley, is becoming quite popular. Making good use of these frameworks requires adapting the algorithms that want to use the features of the system on which they will be deployed, nding optimal parallelization points that leverage the strengths of these frameworks. The correct implementation of algorithms for Big Data platform is a crucial aspect as it will a ect the performance of the algorithm. This Final Master Thesis will focus on the study and development of clustering algorithm Ant Colony Optimization (ACO)[6, 7] on the Spark Apache platform. Multiple tests by clustering simple tasks will be performed for proper validation of the developed system. Once the system is validated and if time permits, system performance will be studied with Big Data problems, such as data clustering on social networks data like Twitter