Download or read book Algorithmes g n tiques written by David Edward Goldberg and published by Addison Wesley France. This book was released on 1994 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Apprentissage par algorithmes genetiques written by Pierre Bonelli and published by . This book was released on 1993 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book APPRENTISSAGE ADAPTATIF ET APPRENTISSAGE SUPERVISE PAR ALGORITHME GENETIQUE written by Gilles Venturini and published by . This book was released on 1994 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: DANS CETTE THESE, NOUS NOUS SOMMES INTERESSES D'UNE PART A UN PROBLEME DE CONTROLE EN ROBOTIQUE ET EN AUTOMATIQUE CARACTERISE PAR DES VARIATIONS IMPREVUES DANS LES MODELES DU ROBOT ET DE SON ENVIRONNEMENT, ET D'AUTRE PART A UN PROBLEME D'APPRENTISSAGE DE REGLES A PARTIR D'UNE BASE D'EXEMPLES COMPORTANT DE NOMBREUSES VALEURS INCONNUES. POUR CES DEUX PROBLEMES, NOUS AVONS UTILISE LES ALGORITHMES GENERIQUES, QUI SONT DES PROCEDURES D'OPTIMISATION INSPIREES DE LA SELECTION NATURELLE, EN ESSAYANT DE LES RENDRE PLUS CONTROLABLES DE MANIERE A TRAITER DES CONNAISSANCES DU DOMAINE. LE PREMIER ALGORITHME ELABORE (AGIL) EST UNE EXTENSION DES SYSTEMES DE REGLES GENETIQUES QUI APPREND DES REGLES DE CONTROLE ET ADAPTE CES REGLES AUX VARIATIONS DU SYSTEME A CONTROLER. IL A ETE TESTE SUR DES PROBLEMES SIMULES. LE DEUXIEME ALGORITHME (SIA) S'INSPIRE DES PRINCIPES DE L'ALGORITHME AQ MAIS EN UTILISANT UN ALGORITHME GENETIQUE COMME MECANISME DE RECHERCHE. IL TRAITE LES VALEURS INCONNUES SANS ESSAYER DE LES REMPLACER. IL A ETE TESTE SUR PLUSIEURS BASES DE DONNEES ET A ETE APPLIQUE A L'ANALYSE D'UN DOMAINE JUDICIAIRE. CES DEUX ALGORITHMES UTILISENT DES HEURISTIQUES EXPLICITES ET PEUVENT TRAITER DIFFERENTS TYPES DE CONNAISSANCES DU DOMAINE
Download or read book M ta apprentissage des algorithmes g n tiques written by Éric Pellerin and published by . This book was released on 2005 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Apprentissage par algorithme g n tique dans la recherche d informations written by Dana Vrajitoru and published by . This book was released on 1996 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Algorithmes genetiques et apprentissage pour la planification de mouvements et la navigation de robots mobiles written by Olivier Pinchard (mathématicien).) and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Algorithmes g n tiques et apprentissage pour la planification de mouvements et la navigation de robots mobiles written by Olivier Pinchard (mathématicien) and published by . This book was released on 1996 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Apprentissage artificiel written by Antoine Cornuéjols and published by Editions Eyrolles. This book was released on 2011-07-07 with total page 837 pages. Available in PDF, EPUB and Kindle. Book excerpt: Les programmes d'intelligence artificielle sont aujourd'hui capables de reconnaître des commandes vocales, d'analyser automatiquement des photos satellites, d'assister des experts pour prendre des décisions dans des environnements complexes et évolutifs (analyse de marchés financiers, diagnostics médicaux...), de fouiller d'immenses bases de données hétérogènes, telles les innombrables pages du Web... Pour réaliser ces tâches, ils sont dotés de modules d'apprentissage leur permettant d'adapter leur comportement à des situations jamais rencontrées, ou d'extraire des lois à partir de bases de données d'exemples. Ce livre présente les concepts qui sous-tendent l'apprentissage artificiel, les algorithmes qui en découlent et certaines de leurs applications. Son objectif est de décrire un ensemble d'algorithmes utiles en tentant d'établir un cadre théorique pour l'ensemble des techniques regroupées sous ce terme "d'apprentissage artificiel". À qui s'adresse ce livre ? Ce livre s'adresse tant aux décideurs et aux ingénieurs qui souhaitent mettre au point des applications qu'aux étudiants de niveau Master 1 et 2 et en école d'ingénieurs, qui souhaitent un ouvrage de référence sur ce domaine clé de l'intelligence artificielle.
Download or read book Genetic Algorithms for Machine Learning written by John J. Grefenstette and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm. The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.
Download or read book Genetic Algorithms written by David E. Goldberg and published by Pearson Education India. This book was released on 2013-02 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, suitable for both course work and self-study, brings together for the first time, in an informal, tutorial fashion, the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields: programmers, scientists, engineers, mathematicians, statisticians and management scientists will all find interesting possibilities here. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. Chapter concludes with exercises and computer assignments. No prior knowledge of Gas or genetics is assumed.
Download or read book Genetic Algorithms in Search Optimization and Machine Learning written by David Edward Goldberg and published by Addison-Wesley Professional. This book was released on 1989 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.
Download or read book Approches volutionnaires pour la reconstruction de r seaux de r gulation g n tique par apprentissage de r seaux bay siens written by Cédric Auliac and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: De nombreuses fonctions cellulaires sont réalisées grâce à l'interaction coordonnée de plusieurs gènes. Identifier le graphe de ces interactions, appelé réseau de régulation génétique, à partir de données d'expression de gènes est l'un des objectifs majeurs de la biologie des systèmes. Dans cette thèse, nous abordons ce problème en choisissant de modéliser les relations entre gènes par un réseau bayésien. Se pose alors la question de l'apprentissage de la structure de ce type de modèle à partir de données qui sont en général peu nombreuses. Pour résoudre ce problème, nous recherchons parmi tous les modèles possibles le modèle le plus simple, expliquant le mieux les données. Pour cela, nous introduisons et étudions différents types d'algorithmes génétiques permettant d'explorer l'espace des modèles. Nous nous intéressons plus particulièrement aux méthodes de spéciation. ces dernières, en favorisant la diversité des solutions candidates considérées, empêchent l'algorithme de converger trop rapidement vers des optima locaux. Ces algorithmes génétiques sont comparés avec différentes méthodes d'apprentissage de structure de réseaux bayésiens, classiquement utilisées dans la littérature. Nous mettons ainsi en avant la pertinence des approches evolutionnaires pour l'apprentissage de ces graphes d'interactions. Enfin, nous les comparons à une classe alternative d'algorithmes évolutionnaires qui s'avère particulièrement prometteuse : les algorithmes à estimation de distribution. Tous ces algorithmes sont testés et comparés sur un modèle du réseau de régulation de l'insuline de 35 noeuds dont nous tirons des jeux de données synthétiques de taille modeste.
Download or read book Foundations and Tools for Neural Modeling written by Jose Mira and published by Springer. This book was released on 2006-12-08 with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes, together with its compagnion LNCS 1607, the refereed proceedings of the International Work-Conference on Artificial and Natural Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 89 revised papers presented were carefully reviewed and selected for inclusion in the book. This volume is devoted to foundational issues of neural computation and tools for neural modeling. The papers are organized in parts on neural modeling: biophysical and structural models; plasticity phenomena: maturing, learning, and memory; and artificial intelligence and cognitive neuroscience.
Download or read book Fouille de donn es par algorithmes g n tiques written by Agnès Braud and published by . This book was released on 2002 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: La fouille de données est un processus visant à la découverte de connaissances potentiellement utiles, enfouies dans les bases de données. Parmi les problématiques de ce domaine, nous nous intéressons à l'apprentissage de concepts à partir d'exemples. Nous nous concentrons sur l'apprentissage de descriptions de concepts exprimées sous forme d'un ensemble de règles, et sur l'apport des algorithmes génétiques dans ce domaine.
Download or read book An Introduction to Genetic Algorithms written by Melanie Mitchell and published by MIT Press. This book was released on 1998-03-02 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
Download or read book Foundations and Tools for Neural Modeling written by Jose Mira and published by Springer Science & Business Media. This book was released on 1999-05-19 with total page 900 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial & Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed & selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation & implementation, image processing & engineering applications.
Download or read book DYNAMIQUE DES POPULATIONS ET POLYMORPHISME DANS LES ALGORITHMES GENETIQUES written by MANUEL.. CLERGUE and published by . This book was released on 1999 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: L'EVOLUTION ARTIFICIELLE, CETTE JEUNE DISCIPLINE DONT LES PREMISSES REMONTENT A LA CYBERNETIQUE DES ANNEES CINQUANTES, S'INSCRIT DANS LE DOMAINE PLUS LARGE DE L'INTELLIGENCE ARTIFICIELLE. ET C'EST BIEN DANS L'OPTIQUE DES SYSTEMES ADAPTATIFS QUE LES ALGORITHMES GENETIQUES ONT ETE DEVELOPPE VOILA VINGT-CINQ ANS PAR LE PROFESSEUR JOHN HOLLAND. LEURS UTILISATIONS TANT POUR L'OPTIMISATION DE FONCTION QUE POUR L'APPRENTISSAGE EN TEMOIGNENT. LES TRAVAUX PRESENTES DANS CE MEMOIRE S'ARTICULENT AUTOUR DE DEUX CONCEPTS CLES : LES COMPORTEMENTS DYNAMIQUES DES POPULATIONS ET LE POLYMORPHISME. L'ETUDE DES ASPECTS DYNAMIQUES EST INCONTOURNABLE SI ON S'INTERESSE AUX ALGORITHMES GENETIQUES EN TANT QUE SYSTEMES ADAPTATIFS. LE POLYMORPHISME EST LIE A CE QUI FAIT LEUR SPECIFICITE : LA MANIPULATION D'UNE POPULATION. NOTRE CONTRIBUTION REPOSE SUR UNE ARCHITECTURE DUALE BASEE SUR L'INTRODUCTION D'UN NIVEAU META DANS L'INTERPRETATION DES INDIVIDUS : CECI PERMET DE REVISITER LA DYNAMIQUE DES POPULATIONS ET LE POLYMORPHISME. LE MAINTIEN DE POPULATIONS STABLES ET POLYMORPHES DANS LE CADRE DE L'OPTIMISATION MULTICRITERE ; L'OBTENTION DE COMPORTEMENTS DYNAMIQUES RICHES ET COMPLEXES DANS LE CADRE DU MODELE MINIMAL DES COMPORTEMENTS DES ALGORITHMES GENETIQUES DUAUX, EN SONT DES EXEMPLES. CES TRAVAUX CONSTITUENT DONC UNE CONTRIBUTION A L'ETUDE DES ALGORITHMES GENETIQUES COMME SYSTEMES ADAPTATIFS POTENTIELLEMENT COMPLEXES.
