CHIP: Constraint Handling with Individual Penalty approach using a hybrid evolutionary algorithm-Reference-Cited by-同舟云学术

CHIP: Constraint Handling with Individual Penalty approach using a hybrid evolutionary algorithm

Published:2018-02-19 Issue:9 Volume:31 Page:5255-5271
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Datta Rituparna^ORCID,Deb Kalyanmoy,Kim Jong-Hwan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-018-3364-x/fulltext.html

Reference27 articles.

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3. Brest J (2009) Constrained real-parameter optimization with $$\varepsilon$$ ε self-adaptive differential evolution. In: Mezura-Montes E (ed) Constraint-handling in evolutionary computation. Springer, Berlin, pp 73–94

4. Datta R, Deb K (2013) Individual penalty based constraint handling using a hybrid bi-objective and penalty function approach. In: 2013 IEEE congress on evolutionary computation (CEC). IEEE, pp 2720–2727

5. Datta R, Deb K (2016) Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization. Soft Comput 20(6):2367–2382

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