Multi-model function optimization based on immune clonal memetic algorithm
免疫文化基因算法求解多模态函数优化问题

For finding all the extreme solutions for multi-model function optimization, this paper proposed an immune clonal optimization memetic algorithm. The algorithm used the danger signal to adaptively guide the clonal, mutation and selection operators. And it also used the Baldwin learning mechanism for the local search, to enhance the searching ability for the best solutions. The experimental results show that the algorithm has higher precise solution.