A hybrid version between differential evolution and the fragment replacement technique was defined for protein structure prediction. The coarse-grained atomic model of the Rosetta system was used for protein representation. The high-dimensional and multimodal nature of protein energy landscapes requires an efficient search for obtaining the native structures with minimum energy. However, the energy model of Rosetta presents an additional difficulty, since the best energy area in the landscape does not necessarily correspond to the closest conformations to the native structure. A strategy is to obtain a diverse set of protein conformations that correspond to different minima in the landscape. The incorporation of the crowding niching method into the hybrid evolutionary algorithm allows addressing the problem of the energy landscape deceptiveness, allowing to obtain a set of optimized and diverse protein folds.

定义了差异进化和片段替换技术之间的混合形式，用于蛋白质结构预测。Rosetta系统的粗粒度原子模型用于蛋白质表示。蛋白质能量分布图的高维和多峰性质要求有效的搜索，以最少的能量获得天然结构。但是，Rosetta的能量模型存在另一个困难，因为景观中的最佳能量区域不一定对应于与自然结构最接近的构象。一种策略是获得与景观中不同极小值相对应的多样化蛋白质构象集。将拥挤小生境方法纳入混合进化算法可以解决能源景观欺骗性的问题，