NSGA-II is one of the most efficient multi-objective evolutionary algorithms (MOEAs) for solving multi-objective optimization problems (MOPs). In this paper, a Jumping Gene Based Learning (JGBL) paradigm is proposed to enhance the exploration ability of NSGA-II. JGBL paradigm simulates the natural behavior of maize and is incorporated into the framework of the original NSGA-II. It only operates on the non-dominated solutions which are eliminated in the environmental selection procedure due to the low quality of crowded distance. The activation of JGBL operation is entirely adapted online according to the search status of evolutionary process to give a needed fuel when the population evolves slowly with inherent variation operators. © 2011 Authors.
|Title of host publication||Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication|
|Publication status||Published - 2011|
- jumping gene based learning
- multi-objective optimization