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Evelyne Lutton Evolutionary Algorithms for Food Science and Technology


Researchers and practitioners in food science and technology routinely face several challenges, related to sparseness and heterogeneity of data, as well as to the uncertainty in the measurements and the introduction of expert knowledge in the models. Evolutionary algorithms (EAs), stochastic optimization techniques loosely inspired by natural selection, can be effectively used to tackle these issues. In this book, we present a selection of case studies where EAs are adopted in real-world food applications, ranging from model learning to sensitivity analysis.

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Dan Simon Evolutionary Optimization Algorithms


A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear—but theoretically rigorous—understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs—including opposition-based learning, artificial fish swarms, bacterial foraging, and many others— and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

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Yoann Pigné Evolutionary Algorithms for Mobile Ad Hoc Networks


Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field. Evolutionary Algorithms for Mobile Ad Hoc Networks: Instructs on how to identify, model, and optimize solutions to problems that arise in daily research Presents complete and up-to-date surveys on topics like network and mobility simulators Provides sample problems along with solutions/descriptions used to solve each, with performance comparisons Covers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and more Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.

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Omid Bozorg-Haddad Meta-heuristic and Evolutionary Algorithms for Engineering Optimization


A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering; Relates optimization algorithms to engineering problems employing a unifying approach. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science. OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran. MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran. HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.

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Hugo Kubinyi Evolutionary Algorithms in Molecular Design


When trying to find new methods and problem-solving strategies for their research, scientists often turn to nature for inspiration. An excellent example of this is the application of Darwin's Theory of Evolution, particularly the notion of the 'survival of the fittest', in computer programs designed to search for optimal solutions to many kinds of problems. These 'evolutionary algorithms' start from a population of possible solutions to a given problem and, by applying evolutionary principles, evolve successive generations with improved characteristics until an optimal, or near-optimal, solution is obtained. This book highlights the versatility of evolutionary algorithms in areas of relevance to molecular design with a particular focus on drug design. The authors, all of whom are experts in their field, discuss the application of these computational methods to a wide range of research problems including conformational analysis, chemometrics and quantitative structure-activity relationships, de novo molecular design, chemical structure handling, combinatorial library design, and the study of protein folding. In addition, the use of evolutionary algorithms in the determination of structures by X-ray crystallography and NMR spectroscopy is also covered. These state-of-the-art reviews, together with a discussion of new techniques and future developments in the field, make this book a truly valuable and highly up-to-date resource for anyone engaged in the application or development of computer-assisted methods in scientific research.

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Alain Petrowski Evolutionary Algorithms


Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.

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Jie Liang Models and Algorithms for Biomolecules and Molecular Networks


By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms. Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms Sampling techniques for estimating evolutionary rates and generating molecular structures Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations End-of-chapter exercises

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Ivan Stojmenovic Handbook of Applied Algorithms


Discover the benefits of applying algorithms to solve scientific, engineering, and practical problems Providing a combination of theory, algorithms, and simulations, Handbook of Applied Algorithms presents an all-encompassing treatment of applying algorithms and discrete mathematics to practical problems in «hot» application areas, such as computational biology, computational chemistry, wireless networks, and computer vision. In eighteen self-contained chapters, this timely book explores: * Localized algorithms that can be used in topology control for wireless ad-hoc or sensor networks * Bioinformatics algorithms for analyzing data * Clustering algorithms and identification of association rules in data mining * Applications of combinatorial algorithms and graph theory in chemistry and molecular biology * Optimizing the frequency planning of a GSM network using evolutionary algorithms * Algorithmic solutions and advances achieved through game theory Complete with exercises for readers to measure their comprehension of the material presented, Handbook of Applied Algorithms is a much-needed resource for researchers, practitioners, and students within computer science, life science, and engineering. Amiya Nayak, PhD, has over seventeen years of industrial experience and is Full Professor at the School of Information Technology and Engineering at the University of Ottawa, Canada. He is on the editorial board of several journals. Dr. Nayak's research interests are in the areas of fault tolerance, distributed systems/algorithms, and mobile ad-hoc networks. Ivan StojmenoviC?, PhD, is Professor at the University of Ottawa, Canada (www.site.uottawa.ca/~ivan), and Chair Professor of Applied Computing at the University of Birmingham, United Kingdom. Dr. Stojmenovic? received the Royal Society Wolfson Research Merit Award. His current research interests are mostly in the design and analysis of algorithms for wireless ad-hoc and sensor networks.

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Christian Blum Optimization Techniques for Solving Complex Problems


Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One—covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more. Part Two—delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more. All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.

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Evelyne Lutton's Home Page

Evelyne Lutton's Home Page. Email: Evelyne.Lutton [AT] inrae.fr. Office postal address: Institut des Systèmes Complexes – Paris Île de France. INRAE-AgroParisTech UMR MIA 518. EKINOCS team. 113 rue Nationale. 75013 PARIS, France. How to come to ISC-PIF. Since ...

dblp: Evelyne Lutton

Evelyne Lutton, Pierrick Legrand, Pierre Parrend, Nicolas Monmarché, Marc Schoenauer: Artificial Evolution - 13th International Conference, Évolution Artificielle, EA 2017, Paris, France, October 25-27, 2017, Revised Selected Papers. Lecture Notes in Computer Science 10764, Springer 2018, ISBN 978-3-319-78132-7

Evelyne Lutton - IEEE Xplore Author Details

evolutionary computation,genetic algorithms,image reconstruction,medical image processing,automatic test software,biological organs,computerised tomography,conjugate ...

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Evelyne Lutton has written 12 work(s) Search for other authors with the same name. displaying 1 to 12 | at end show results in order: alphabetically | oldest to newest | newest to oldest. Artificial Evolution: 12th International Conference, Evolution Artificielle, Ea 2015, Lyon, France, October 26-28, 2015. Revised Selected Papers . Product Description: This book constitutes the thoroughly ...

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Evelyne Lutton - FreeTechBooks

Evelyne Lutton. No information is available for this author. Back to all authors. Follow; Books Authored by Evelyne Lutton. Genetic and Evolutionary Computation for Image Processing and Analysis. Post date: 01 Apr 2008 This book is the first attempt to offer a panoramic view on the field of image analysis and processing in which genetic and evolutionary computation (GEC) techniques are applied ...

Evelyne Lutton (Author of Artificial Evolution)

Evelyne Lutton is the author of Artificial Evolution (0.0 avg rating, 0 ratings, 0 reviews), Evolutionary Algorithms for Food Science and Technology (0.0...

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Évelyne Lutton; Philosophy; 1990; Notre but est de proposer un systeme qui permettre de retrouver la position et l'orientation d'une camera de prise de vue, a partir d'une seule photographie, connaissant par ailleurs un modele de la … Expand. 1. Cite Save Feed. The Allen Institute for AI. Proudly built by AI2 with the help of our Collaborators using these Sources. Research•Publisher ...

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Evelyne Lutton's research works | Université Paris-Saclay ...

Evelyne Lutton's 211 research works with 1,803 citations and 5,634 reads, including: Monitoring food structure during digestion using small-angle scattering and imaging techniques

Evelyne Lutton - Bangor University

Monitoring of the stomach content based on MRI data. 25 Feb 2019 → 15 Mar 2019. Hosting an academic visitor

Evelyne Lutton | Open Library

Evelyne Lutton. 3 works Add another? Most Editions | First Published | Most Recent. Showing all works by author. Would you like to see only ebooks? Artificial Evolution by Evelyne Lutton, Pierrick Legrand, Pierre Parrend, Nicolas Monmarch é, Marc Schoenauer. First published in 2012 5 editions ...

Books by Evelyne Lutton (Author of Artificial Evolution)

Evelyne Lutton has 23 books on Goodreads with 26 ratings. Evelyne Lutton’s most popular book is Fractals in Engineering: New Trends in Theory and Applica...

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Cooperative coevolution for agrifood process modeling

Olivier Barri`ere and Evelyne Lutton INRIA Saclay - Ile-de-France, Parc Orsay Universit´e, 4, rue Jacques Monod, 91893 Orsay Cedex, France, e-mail:[email protected],[email protected] Pierre-Henri Wuillemin LIP6-CNRS UMR7606, 75016 Paris, France C´edric Baudrit, Mariette Sicard and Nathalie Perrot UMR782 Genie et Microbiologie des Proc´ ed´ es Alimentaires. AgroParisTech, INRA ...

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View Evelyne Lutton's profile on Publons with 64 publications. View Evelyne Lutton's profile on Publons with 64 publications. COVID-19 : add an open review or score for a COVID-19 paper now to ensure the latest research gets the extra scrutiny it needs.

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Fractals in Engineering New Trends in Theory and Applications by Evelyne Lutton, Jacques Lévy-Véhel, Jacques Levy-Vehel, Kofi Kissi Dompere, Évelyne Lutton Hardcover, 290 Pages, Published 2005 by Springer ISBN-13: 978-1-84628-047-4, ISBN: 1-84628-047-8

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Lutton, Evelyne. Treffer 1 - 8 von 8. Treffer pro Seite. Sortieren. 1 . Online-Ressource. Evolutionary Algorithms for Food Science and Technology. von Lutton, Evelyne. veröffentlicht 2016 “... Lutton, Evelyne.... ” Signatur: Wird geladen ... Standort: Wird geladen ... Click Here Please ...

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Person: Evelyne Lutton DBLP: Lutton:Evelyne Contributed to: 2005. 2003. Wrote 2 papers: SAC-2005-ValigianiBJLBC Experimenting with a real-size man-hill to optimize pedagogical paths (GV, RB, YJ, EL, CBR, PC), pp. 4–8. SAC-2003-Landrin-SchweitzerCPL #algorithm #interactive Introducing Lateral Thinking in Search Engines with Interactive Evolutionary Algorithms (YLS, PC, TP, EL), pp. 214–219 ...

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Fractals in Engineering : New Trends in Theory and ...

[(Fractals in Engineering : New Trends in Theory and Applications)] [Edited by Evelyne Lutton ] published on (August, 2006) | Evelyne Lutton | ISBN: | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

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Artificial Evolution: Third European Conference, AE '97 ...

Artificial Evolution: Third European Conference, AE '97, Nimes, France, October 22-24, 1997, Selected Papers (Lecture Notes in Computer Science (1363), Band 1363) | Dominique Snyers, Evelyne Lutton, Marc Schoenauer, Jin-Kao Hao, Edmund Ronald | ISBN: 9783540641698 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Harding Jenny A. Evolutionary Computing in Advanced Manufacturing


This cutting-edge book covers emerging, evolutionary and nature inspired optimization techniques in the field of advanced manufacturing. The complexity of real life advanced manufacturing problems often cannot be solved by traditional engineering or computational methods. Hence, in recent years researchers and practitioners have proposed and developed new strands of advanced, intelligent techniques and methodologies. Evolutionary computing approaches are introduced in the context of a wide range of manufacturing activities, and through the examination of practical problems and their solutions, readers will gain confidence to apply these powerful computing solutions. The initial chapters introduce and discuss the well established evolutionary algorithm, to help readers to understand the basic building blocks and steps required to successfully implement their own solutions to real life advanced manufacturing problems. In the later chapters, modified and improved versions of evolutionary algorithms are discussed. The book concludes with appendices which provide general descriptions of several evolutionary algorithms.

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Evelyne Striok L.E.D.E.T.

Бруно Гранихштадтен Evelyne

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Pawel Cichosz Data Mining Algorithms


Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

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Evelyne Weissenbach Tod eines Surfers

Evelyne Weissenbach Tod eines Weinbauern

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