Insights on Transfer Optimization: Because Experience is the Best Teacher

Traditional optimization solvers tend to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of solvers do not automatically grow with experience. In contrast, however, humans routinely make use of a pool of knowledge drawn from...

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Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 2; H. 1; S. 51 - 64
Hauptverfasser:	Gupta, Abhishek, Ong, Yew-Soon, Feng, Liang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.02.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial intelligence Bayes methods Bayesian optimization Computational intelligence Computer science evolutionary algorithms Knowledge management Knowledge transfer multiform optimization Multitasking Optimization Problem-solving Solvers Transfer
ISSN:	2471-285X, 2471-285X
Online-Zugang:	Volltext
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