Elitist Evolutionary Algorithm

Elitist evolutionary algorithms (EAs) maintain the best-performing solutions throughout the optimization process, a strategy whose efficiency is currently under intense scrutiny. Research focuses on analyzing the runtime performance of elitist EAs on various benchmark problems, particularly multimodal functions with local optima, using techniques like fitness level analysis and drift analysis to derive tighter performance bounds. These analyses reveal that while elitism is often effective, its performance can be significantly impacted by the problem landscape and that alternative approaches, such as incorporating global mutation operators or non-elitist strategies, can offer advantages in specific scenarios. This ongoing research contributes to a deeper understanding of EA behavior and informs the development of more robust and efficient optimization methods.