中文摘要：

学习进化经验并用于指导进化对人工免疫算法这样的随机搜索类算法十分重要。Memetic算法在进化算子中引入局部搜索，算法的学习机制决定哪种局部搜索机制适合目标问题。然而，这类算法需要使用者事先提供问题相关的局部搜索策略。为了克服Memetic算法的这一缺点，针对函数优化问题提出了一种基于蚁群信息素的无指导的学习机制，并在此基础之上构造了基于信息素模因的克隆选择算法。算法无需提供候选的局部搜索策略（即模因），学习的内容是抗体的进化趋势，而并非要确定合适的局部搜索策略。实验结果表明，信息素模因学习机制借助信息素浓度的收敛学习到了关于目标函数的有用信息，有效提高了克隆选择算法的搜索效率。

英文摘要：

In order to make the artificial immune algorithms more efficient, learning mechanisms which obtain experiences from population＇s previous evolution and use them as a guide for further developments are very important for this kind of stochastic searching algorithms. To address this, Memetic algorithms combine local search heuristics with evolutionary operators and their learning mechanisms make a decision about which heuristic to be suitable for the target problem. However, users have to provide several problem dependent heuristics in advance. In this paper, an unsupervised learning mechanism based on ant colony pheromone is designed for the function optimization problem. Based on the proposed learning mechanism, a novel Memetic algorithm termed pheromone meme based clonal selection algorithm （PM_CSA） is also put forward. In MP CSA, the pheromone is used as a carrier of evolutionary experiences, and the concentration distribution of the pheromone acts as a guider for generating new individuals. Different from conventional Memetic algorithms, the pheromone based learning is not to make a choice of which predefined meme will be employed but to obtain developing experience from evolution. Experimental results indicate that the pheromone based learning has the ability of acquiring useful information about the objective functions. It significantly improves the performance of standard clonal selection algorithm.