遺傳算法的參數(shù)分析-文庫吧資料
2024-09-12 17:24本頁面
【正文】 生物學基礎 本章主要介紹了遺傳算法產(chǎn)生的背景，遺傳算法的發(fā)展及研究狀況，以及本文的主要工作。 關鍵詞 ： 非數(shù)值優(yōu)化問題 ， 思維進化計算 ， 趨同 ， 異化 ， 信息矩陣 太原科技大學畢業(yè)設計（論文） IV Inheritance Algorithmic Parameter Analysis ABSTRACT Mind Evolutionary Computation(MEC) was proposed by simulating the processes of human mind. It is a new potential evolutionary algorithm. MEC has been applied to numerical optimization problems, and some nonnumerical optimization problems, for example traveling salesman problem, jobshop scheduling, and Modeling for Systems of Ordinary Differential Equations, are solved successfully with MEC. But the allpurpose algorithm of MEC for nonnumerical problems doesn’t exist. In this paper, MEC algorithm is introduced for a kind of nonnumeric optimization problems which solution space is limit. First an allpurpose coding method is induced according to the mon characteristics of those problems. Then a series of concepts ,for example character ,information matrix， etc， are introduced. So an allpurpose similartaxis and dissimilation operations of MEC for those problems are designed. Consequently MEC algorithm for a kind of nonnumeric optimization problems is introduced and its global convergence is proved with binatorial theory and Markov chain. We solve vertex coloring problem and jobshop scheduling with this algorithm. Our experiments show that this algorithm is feasible and effective. This algorithm is allpurpose and it is fitted for traveling salesman problem, jobshop scheduling, vertex coloring problem, the optimization of the artificial neural work architecture and Modeling for Systems, etc. When we solve a nonnumerical problem with this algorithm, if this problem is converted reasonably and the character and information matrix of this problem ar
公司管理相關推薦
鄂ICP備17016276號-1