采用信息熵的自適應(yīng)遺傳算法

為解決傳統(tǒng)遺傳算法容易早熟及收斂速度慢的缺陷，在分析了多樣性的重要性后，提出了一種新的基于信息熵的遺傳策略，該策略在保留最優(yōu)個(gè)體的基礎(chǔ)上，根據(jù)當(dāng)前種群個(gè)體熵與種群熵的變化自適應(yīng)調(diào)整遺傳算子的各項(xiàng)參數(shù)，將種群的內(nèi)部狀態(tài)與遺傳操作有機(jī)地結(jié)合起來(lái)，使得種群多樣性得到保證，提高算法的全局搜索能力．試驗(yàn)結(jié)果表明了該方法在運(yùn)行過(guò)程中能避免早熟的發(fā)生，在處理復(fù)雜問(wèn)題時(shí)表現(xiàn)出較高的性能．

To solve the problem of premature convergence and slow convergence flaws in the traditional genetic algorithm ，a new genetic strategy which is based on inform ation entropy is proposed based on analyzing the importance of the diversity．In this strategy，the algorithm will adaptively adj ust its operator parameters in accordance with the current individual entropy and the population entropy based on the reserved optim al individua1．It organically combines the internal state of population and the genetic operation to guarantee the diversity of the population and improve the global search capabilities．The result showed that this method can avoid the occurrence of premature convergence and provides excellent performance in dealing com plexproblems．