Real-Coded Genetic Algorithms (RCGAs) have been extensively studied for last two decades because RCGAs have advantages over conventional continuous function optimization methods when multimodal functions are optimized. Innately Split Model (ISM) is one of promising approaches to enhance RCGAs where a set of population groups are evolved in parallel and groups are re-initialized if two groups searches a similar region (it is called redundant searches). In this paper, we propose a new strategy for the re-initialization of groups to improve the performance of ISM. In our method, redundant searches are detected by using the information of the search histories of the groups. This information is called traps and is stored as a set of hyper-ellipsoids representing the distributions of the previous groups. We demonstrate that the proposed method is robust and superior to the original ISM.
展开▼
