Solution Transfer in Evolutionary Optimization: An Empirical Study on Sequential Transfer
Xiaoming Xue, Cuie Yang, Liang Feng, Kai Zhang, Linqi Song, Kay Chen Tan
Abstract
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 paper: 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 is of great importance in designing what, when and how to transfer towards 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.