.png){kind=icon}
Return
Evolutionary Algorithms
.png){kind=icon}
PRE
AI
DOI：10.1002/widm.1124.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. On the basis of the evolutionary cycle, similarities and differences between these algorithms are described. We briefly discuss how EAs can be adapted to work well in case of multiple objectives, and dynamic or noisy optimization problems. We look at the tuning of algorithms and present some recent developments coming from theory. Finally, typical applications of EAs to real-world problems are shown, with special emphasis on data-mining applications. WIREs Data Mining Knowl Discov 2014, 4:178-195. doi: 10.1002/widm.1124 Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the .
Keywords:
MULTIOBJECTIVE OPTIMIZATION
GENETIC ALGORITHM
COMPUTATION
STRATEGIES
TOOL
Journal
IF:
11.7
Papers:
549
Citations:
5.3K
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveQTL mapping for drought-tolerance coefficient of seedling related traits during wheat germination.png)
IF0
PREAI
ShareSave
ShareSaveShareSave
View more