Reference sharing: a new collaboration model for cooperative coevolution
Journal of Heuristics, ISSN: 1572-9397, Vol: 23, Issue: 1, Page: 1-30
2017
- 19Citations
- 15Captures
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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.
Article Description
Cooperative coevolutionary algorithms have been a popular and effective learning approach to solve optimization problems through problem decomposition. However, their performance is highly sensitive to the degree of problem separability. Different collaboration mechanisms usually have to be chosen for particular problems. In the paper, we aim to design a collaboration model that can be successfully applied to a wide range of problems. We present a novel collaboration mechanism that offers this type of potential, along with a new sorting strategy for individuals that are assigned multiple fitness values. Furthermore, we demonstrate and analyze our algorithm through comparison studies with other popular cooperative coevolutionary models on a suite of standard function optimization problems.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85010808949&origin=inward; http://dx.doi.org/10.1007/s10732-016-9322-9; http://link.springer.com/10.1007/s10732-016-9322-9; http://link.springer.com/content/pdf/10.1007/s10732-016-9322-9.pdf; http://link.springer.com/article/10.1007/s10732-016-9322-9/fulltext.html; https://dx.doi.org/10.1007/s10732-016-9322-9; https://link.springer.com/article/10.1007/s10732-016-9322-9
Springer Science and Business Media LLC
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