在子集條件約束下的最大熵分布及其應(yīng)用

介紹了有關(guān)熵的概念及計(jì)算方法，并將其應(yīng)用于構(gòu)建一類(lèi)新的分布估計(jì)算法(EDAs)。該類(lèi)分布估計(jì)算法用基于最大熵估計(jì)種群中的模式概率分布和從最大熵分布中抽樣取代遺傳算法(GA)的交叉和變異，產(chǎn)生新的種群。在該類(lèi)算法中，二階連接模式算法由于只使用了連接模式，在解決變量之間相互作用趨向于發(fā)生在串中相互靠近的變量之間的一類(lèi)問(wèn)題時(shí)，比遺傳算法更好。
關(guān) 鍵 詞 熵; 抽樣; 模式; 模式族

Abstract After introducing some concepts and computations of entropy, a new type of estimation of distribution algorithms (EDAs) is developed by using principle of maximum entropy. This type of algorithms replaces the crossover and mutation operators used by genetic algorithm (GA) with the estimation of the maximum entropy distribution of schema in the population and sampling from maximum entropy distribution to generate new population. Among this type of algorithms, only contiguous schemata are used in order-2 contiguous schemata algorithm. Therefore, order-2 contiguous schemata algorithm may work better than GA when interactions between variables tend to be between variables that are located close to each other on the string.
Key words entropy; sampling; schema; schema family