We are analyzing https://link.springer.com/chapter/10.1007/3-540-44719-9_16.

Title:
A Statistical Comparison of Multiobjective Evolutionary Algorithms Including the MOMGA-II | SpringerLink
Description:
Many real-world scientific and engineering applications involve finding innovative solutions to “hard” Multiobjective Optimization Problems (MOP). Various Multiobjective Evolutionary Algorithms (MOEA) have been developed to obtain MOP Pareto solutions. A...
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Education
Science
Technology & Computing

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month

Based on our best estimate, this website will receive around 7,626,432 visitors per month in the current month.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Link.springer.com Make Money? {💸}

We can't tell how the site generates income.

Not every website is profit-driven; some are created to spread information or serve as an online presence. Websites can be made for many reasons. This could be one of them. Link.springer.com could be getting rich in stealth mode, or the way it's monetizing isn't detectable.

Keywords {🔍}

multiobjective, evolutionary, algorithms, google, scholar, david, optimization, genetic, van, veldhuizen, lamont, algorithm, gary, computer, engineering, messy, chapter, institute, technology, deb, privacy, cookies, content, information, journal, publish, research, search, conference, paper, momgaii, download, pages, multicriterion, science, momga, results, preview, access, article, air, force, afb, kalyanmoy, zitzler, thiele, coello, springer, analysis, personal,

Topics {✒️}

multi-objective messy ga evolutionary multi-criterion optimization multi-objective optimization single-objective building block month download article/chapter messy genetic algorithms momga-ii conference paper multiobjective evolutionary algorithms privacy choices/manage cookies nature-ppsn vi population referred device instant download strength pareto approach chapter usd 29 evolutionary algorithm air force institute european economic area real-world scientific probabilistically complete initialization computational bottle-necks parallel problem solving instituto politecnico nacional san pedro zacatenco swiss federal institute wright-patterson afb conditions privacy policy check access download preview pdf ethics access journal finder publish chapter log accepting optional cookies probabilistic bb approach reading rg6 6ay coello coello multiobjective optimization technical report tr-98-03 main content log paper cite computer engineering book series affiliations department conference series international conference similar statistical results permissions reprints paper zydallis accurate optimization kalyanmoy deb lothar thiele

Schema {🗺️}

ScholarlyArticle:
      headline:A Statistical Comparison of Multiobjective Evolutionary Algorithms Including the MOMGA-II
      pageEnd:240
      pageStart:226
      image:https://media.springernature.com/w153/springer-static/cover/book/978-3-540-44719-1.jpg
      genre:
         Computer Science
         Computer Science (R0)
      isPartOf:
         name:Evolutionary Multi-Criterion Optimization
         isbn:
            978-3-540-44719-1
            978-3-540-41745-3
         type:Book
      publisher:
         name:Springer Berlin Heidelberg
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Jesse B. Zydallis
            affiliation:
                  name:Air Force Institute of Technology
                  address:
                     name:Dept of Electrical and Computer Engineering, Air Force Institute of Technology, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:David A. Van Veldhuizen
            affiliation:
                  name:Air Force Research Laboratory
                  address:
                     name:Optical Radiation Branch, Air Force Research Laboratory, TX, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Gary B. Lamont
            affiliation:
                  name:Air Force Institute of Technology
                  address:
                     name:Dept of Electrical and Computer Engineering, Air Force Institute of Technology, USA
                     type:PostalAddress
                  type:Organization
            type:Person
      keywords:Pareto Front, Test Suite, Generational Distance, Pareto Optimal Solution, Multiobjective Problem
      description:Many real-world scientific and engineering applications involve finding innovative solutions to “hard” Multiobjective Optimization Problems (MOP). Various Multiobjective Evolutionary Algorithms (MOEA) have been developed to obtain MOP Pareto solutions. A particular exciting MOEA is the MOMGA which is an extension of the single-objective building block (BB) based messy Genetic Algorithm. The intent of this discussion is to illustrate that modifications made to the Multi-Objective messy GA (MOMGA) have further improved its efficiency resulting in the MOMGA-II. The MOMGA-II uses a probabilistic BB approach to initializing the population referred to as Probabilistically Complete Initialization. This has the effect of improving the efficiency of the MOMGA through the reduction of computational bottle-necks. Similar statistical results have been obtained using the MOMGA-II as compared to the results of the original MOMGA as well as those obtained by other MOEAs as tested with standard generic MOP test suites.
      datePublished:2001
      isAccessibleForFree:
      hasPart:
         isAccessibleForFree:
         cssSelector:.main-content
         type:WebPageElement
      context:https://schema.org
Book:
      name:Evolutionary Multi-Criterion Optimization
      isbn:
         978-3-540-44719-1
         978-3-540-41745-3
Organization:
      name:Springer Berlin Heidelberg
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:Air Force Institute of Technology
      address:
         name:Dept of Electrical and Computer Engineering, Air Force Institute of Technology, USA
         type:PostalAddress
      name:Air Force Research Laboratory
      address:
         name:Optical Radiation Branch, Air Force Research Laboratory, TX, USA
         type:PostalAddress
      name:Air Force Institute of Technology
      address:
         name:Dept of Electrical and Computer Engineering, Air Force Institute of Technology, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Jesse B. Zydallis
      affiliation:
            name:Air Force Institute of Technology
            address:
               name:Dept of Electrical and Computer Engineering, Air Force Institute of Technology, USA
               type:PostalAddress
            type:Organization
      name:David A. Van Veldhuizen
      affiliation:
            name:Air Force Research Laboratory
            address:
               name:Optical Radiation Branch, Air Force Research Laboratory, TX, USA
               type:PostalAddress
            type:Organization
      name:Gary B. Lamont
      affiliation:
            name:Air Force Institute of Technology
            address:
               name:Dept of Electrical and Computer Engineering, Air Force Institute of Technology, USA
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Dept of Electrical and Computer Engineering, Air Force Institute of Technology, USA
      name:Optical Radiation Branch, Air Force Research Laboratory, TX, USA
      name:Dept of Electrical and Computer Engineering, Air Force Institute of Technology, USA
WebPageElement:
      isAccessibleForFree:
      cssSelector:.main-content

External Links {🔗}(48)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js

4.1s.

LINK . SPRINGER . COM {}