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Monday, July 27, 2020 | History

5 edition of Applications of multi-objective evolutionary algorithms found in the catalog.

Applications of multi-objective evolutionary algorithms

Applications of multi-objective evolutionary algorithms

  • 322 Want to read
  • 12 Currently reading

Published by World Scientific in Hackensack, NJ .
Written in English

    Subjects:
  • Combinatorial optimization.,
  • Evolutionary computation.,
  • Genetische algoritmen.

  • Edition Notes

    Includes bibliographical references and index.

    Statementeditors, Carlos A. Coello Coello, Gary B. Lamont.
    SeriesAdvances in natural computation -- v. 1
    ContributionsCoello Coello, Carlos A., Lamont, Gary B.
    Classifications
    LC ClassificationsQA402.5 .A6428 2004
    The Physical Object
    Paginationxxvii, 761 p. :
    Number of Pages761
    ID Numbers
    Open LibraryOL18005496M
    ISBN 109812561064
    ISBN 109789812561060
    LC Control Number2007297637

    Abstract. Evolutionary algorithms (EA s) have amply shown their promise in solving various search and optimization problems for the past three of the hallmarks and niches of EA s is their ability to handle multi-objective optimization problems in their totality, which their classical counterparts lack. Suggested in the beginning of the s, evolutionary multi Cited by: Real-World Applications of Genetic Algorithms. The book addresses some of the most recent issues, with the theoretical and methodological aspects, of evolutionary multi-objective optimization problems and the various design challenges using Cited by:

    Evolutionary Multi-Objective Algorithms, Real-World Applications of Genetic Algorithms, Olympia Roeva, IntechOpen, DOI: / Available from: Aurora Torres, Dolores Torres, Sergio Enriquez, Eunice Ponce de León and Elva Díaz (March 7th ).Cited by: 1. About this title: This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications.

    In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications. Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device : Kay Chen Tan, Eik Fun Khor, Tong Heng Lee.


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Applications of multi-objective evolutionary algorithms Download PDF EPUB FB2

This item: Applications Of Multi-Objective Evolutionary Algorithms (Advances In Natural Computation) Set up a giveaway. Get fast, free delivery with Amazon Prime.

Prime members enjoy FREE Two-Day Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle by: This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design.

Many complex engineering optimization problems can be modelled as multi-objective : Hardcover. This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs).

The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design.

Many complex engineering optimization problems can be modelled as multi-objective formulations. fast growing field of multi-objective optimization, especially its applications in many disciplines, from engineering design to spectroscopic data analysis, from groundwater monitor to regional planning, from autonomous vehicle navigation to polymer extrusion, and from bioinformatics to.

Here, multi-objective evolutionary algorithms (MOEAs) are applied to generate a set of MFRBSs with different trade-offs between interpretability and accuracy.

In MOEFSs interpretability has often Author: Carlos A. Brizuela. The application of Multi-Objective Evolutionary Algorithm concepts to this area has started more recently compared to the vast majority of other application areas, as e.

design and engineering. We give a brief survey on promising developments within this field and discuss potential future research directions. Applications of Multi-Objective Evolutionary Algorithms - [Book Review] Published in: IEEE Computational Intelligence Magazine (Volume: 1, Issue: 1, Feb.

) Article #: Page(s): 43 - Date of Publication: 21 February ISSN Information: Print ISSN: X Cited by: 3. Applications of Multi-Objective Evolutionary Algorithms - [Book Review. An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach Using a.

Get this from a library. Applications of multi-objective evolutionary algorithms. [Carlos A Coello Coello; Gary B Lamont;] -- "This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs).

The topics. Applications Of Multi-Objective Evolutionary Algorithms (Advances in Natural Computation) Carlos A. Coello Coello, Gary B. Lamont, Carlos A. Coello This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs).

evolutionary multi-objective optimization (EMO) algorithms is now an established eld of research and application with many dedicated texts and edited books, commercial softwares and numerous freely downloadable codes, a biannual conference series running successfully sincespecial sessions andFile Size: KB.

Get this from a library. Applications of multi-objective evolutionary algorithms. [Carlos A Coello Coello; Gary B Lamont;] -- This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs).

The topics. "Industrial applications of evolutionary algorithms" is intended as a resource for both experienced users of evolutionary algorithms and researchers that are beginning to approach these fascinating optimization enced users will find interesting details of real-world problems.

This book describes how evolutionary algorithms (EA), along with genetic algorithms (GA) and particle swarm optimization (PSO) may be utilized for fixing multi-objective optimization points in the world of embedded and VLSI system design.

Many difficult engineering optimization points could possibly be modelled as multi-objective formulations. The classical methods usually aim at a single solution while the evolutionary methods provide a whole set of so-called Pareto-optimal solutions. Evolutionary Multi-Objective System Design: Theory and Applications.

provides a representation of the state-of-the-art in evolutionary multi-objective optimization research area and related new trends. MOEAs are very powerful techniques that have been applied successfully in numerous applications and multiple types of optimization, search and machine learning problems.

This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in “Applications of Multi-Objective Evolutionary Algorithms”. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions.

It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems: /ch Antenna and microwave design problems are, in general, multi-objective.

Multi-objective Evolutionary Algorithms (MOEAs) are suitable optimization techniquesCited by: 1. The book has also been conceived for professionals interested in developing practical applications of evolutionary algorithms to real-world multi-objective optimization problems.

Each chapter is complemented by discussion questions and several ideas that attempt to trigger novel research paths.which algorithms are suited to which kind of problem, and what the specific advantages and drawbacks of certain methods are.

The subject of this work is the comparison and the improvement of existing multiobjective evolutionary algorithms and their application to system design problems in computer engineering.

In detail, the major File Size: 2MB.This book brings together the latest findings from the leading researchers in the field for obtaining efficient solutions of multi-objective optimization problems and focuses on real-world optimization problems by using a wide spectrum of strategies encompassing evolutionary to hybrid .