Evolutionary algorithms and their applications to engineering problems
Neural Computing and Applications, ISSN: 1433-3058, Vol: 32, Issue: 16, Page: 12363-12379
2020
- 468Citations
- 567Captures
- 5Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Review Description
The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85082669675&origin=inward; http://dx.doi.org/10.1007/s00521-020-04832-8; http://link.springer.com/10.1007/s00521-020-04832-8; http://link.springer.com/content/pdf/10.1007/s00521-020-04832-8.pdf; http://link.springer.com/article/10.1007/s00521-020-04832-8/fulltext.html; https://dx.doi.org/10.1007/s00521-020-04832-8; https://link.springer.com/article/10.1007/s00521-020-04832-8
Springer Science and Business Media LLC
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