Enhancement of the shifting balance genetic algorithm for highly multimodal problems
Jun shuo Chen, Mark Wineberg · 2004
The shifting balance genetic algorithm (SBGA) is an extension of the genetic algorithm (GA) that was created to promote guided diversity to improve performance in highly multimodal environments. Based on a new behavioral model for the SBGA, various modifications are proposed: these include a mechanism for managing dynamic population sizes with population restarts, and communication among the colonies. The enhanced SBGA is compared against the original SBGA system and other multipopulational GA systems on a complex mathematical function (F8F2) and on the NP-complete 0/1 knapsack problem. In all cases, the enhanced SBGA outperformed all other systems, and on the 0/1 knapsack problem, it was the only one to find the global optimum.