One approach for evolutionary algorithms (EAs) to address dynamic optimization problems (DOPs) is to maintain diversity of the population via introducing immigrants. So far all immigrant schemes developed for EAs have used fixed replacement rates. This paper examines the impact of the replacement rate on the performance of EAs with immigrant schemes in dynamic environments, and proposes a self-adaptive mechanism for EAs with immigrant schemes to address DOPs. Our experimental study showed that the new approach could avoid the tedious work of fine-tuning the parameter and outperformed other immigrant schemes using a fixed replacement rate with traditionally suggested values in most cases. © 2011 Science China Press and Springer-Verlag Berlin Heidelberg.
Bibliographical noteThis work was partially supported by the National Natural Science Foundation of China (Grant No. U0835002), the Fund for Foreign Scholars in University Research and Teaching Programs (Grant No. B07033), an EPSRC (Grant No. EP/E058884/1) on “Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications”, and the Fund for Creative Research for Graduate Students of University of Science and Technology of China (Grant No. KD0901103).
- dynamic optimization problem
- evolutionary algorithm
- immigrant scheme
- self-adaptive replacement rate