Testing an evolutionary portfolio algorithm on the CEC2016 real-parameter single objective optimization
Junyan Yi, Zheng He, Gang Yang · 2016
In order to take advantages of evolutionary algorithms inspired by different biological evolutions, varieties of approaches have been proposed to combine them together. One of them is the portfolio approach, which keeps choosing a component algorithm from a portfolio of evolutionary algorithms (EAs) to run during the optimizing process. In our approach, each component algorithm has its own population and runs independently without information exchange. At the beginning of each generation, only the component algorithm with the best predicted performance is allowed to run. The proposed portfolio approach is tested on the CEC2016 real-parameter single objective optimization benchmarks. The results show that it is competitive.