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Solution Transfer in Evolutionary Optimization: An Empirical Study on Sequential Transfer
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AI
DOI：10.1109/TEVC.2023.3339506.png)
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Abstract
En 中文
Knowledge transfer from optimized problems has emerged as a promising technique for enhancing evolutionary search. However, most studies in this domain primarily concentrate on devising knowledge transfer mechanisms for specific problem domains, often lacking the examination of the fundamental aspects of knowledge transfer, i.e., what, when, and how to transfer across diverse scenarios. This not only restricts the generality of these algorithms but also hinders their practical applicability. In light of this, this article: 1) reviews a vast array of techniques associated with the crucial aspects of solution transfer and 2) conducts a series of experiments to explore the underlying transfer mechanisms that enhance the evolutionary search. In particular, we first define solution transferability in the context of evolutionary search, which provides a new perspective in understanding what, when, and how to transfer in enhancing evolutionary search. Next, through comprehensive experiments, we find that the approximation and evaluation of solution transferability are of great importance in designing what, when, and how to transfer toward enhanced evolutionary search. Furthermore, our empirical study also discusses the counterintuitive performance improvements unrelated to the search experience of source tasks. The source code for reproducing our experiments is available at https://github.com/XmingHsueh/ STO-EC.
Keywords:
Task analysis
Optimization
Knowledge transfer
Timing
Measurement
Evolutionary computation
Urban areas
How to transfer
transfer optimization
transferability
what to transfer
when to transfer
Journal
IF:
12
Papers:
1.8K
Citations:
2.4W
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