general sequencing 中文意思是什麼
general sequencing
解釋
常規序列- general : adj （opp special）1 一般的，綜合的，通用的。2 普通的，廣泛的，通常的。3 全體的，總的；全面的，普...
- sequencing : 測序，序列測定
-
In general, these studies are primarily based on the theories of financial restraint and financial deepening initiated by r. i. mckinnon and e. s. shaw, either stressing the urgency of china ’ s interest rate liberalization, its international experience, target orientation, mode selection, sequencing, conditional creation, risk control and the transformation of the monetary policy conduction mechanism, or such problems as the effect of reform on each economic party, positive examination of the real interest rate, savings mobilization, investment quality, relativity between the variables in economic growth as well as the interest rate sensibility in economic sectors of different ownerships
總的看來，這些研究基本上以麥金農和肖所開創的金融抑制?金融深化理論為依據，或是側重於討論我國利率市場化改革的必要性迫切性、國際經驗、目標定位、模式選擇、次序安排、條件創造、風險控制以及貨幣政策傳導機制的改造等問題，或是側重於分析改革對各個經濟行為主體的影響，再者就是實證考察實際利率、儲蓄動員、投資質量、經濟增長各個變量之間的相關性和不同所有制經濟部門的利率敏感性。 -
A modified genetic algorithm ( mga ) framework was developed and applied to the flowshop sequencing problems with objective of minimizing mean total flowtime. to improve the general genetic algorithm routine, two operations were introduced into the framework. firstly, the worst points were filtered off in each generation and replaced with the best individuals found in previous generations ; secondly, the most promising individual was selectively cultivating if a certain number of recent generations have not been improved yet. under conditions of flowshop machine, the initial population generation and crossover function can also be improved when the mga framework is implemented. computational experiments with random samples show that the mga is superior to general genetic algorithm in performance and comparable to special - purpose heuristic algorithms. the mga framework can also be easily extended to other optimizations even though it will be implemented differently in detail
提出了一個改進遺傳演算法的結構，並且應用於帶有目標是最小平均總流程時間的流水調度排序中.為了改進一般遺傳演算法的程序，兩個新的操作被引進到這個操作中.這兩個操作為： 1 ）過濾操作：過濾掉在每一代中的最壞的個體，用前一代中的最好的個體替代它； 2 ）培育操作：當在一定代數內演算法不改進時，選擇一個培育操作用於培育最有希望的個體.通過大量的隨機產生的問題的例子的計算機實驗顯示出，提出的演算法的性能明顯好於一般遺傳演算法，並且和此問題的最好的專門意義的啟發式演算法相匹配.新的mga框架很容易擴展到其它最優化當中，只是實施的詳細的步驟有所不同
分享友人