隨機遺傳過程 的英文怎麼說

中文拼音 [suíjīyízhuànguòchéng]
隨機遺傳過程 英文
stochastic genetic process
  • 隨 : Ⅰ動詞1 （跟; 跟隨） follow 2 （順從） comply with; adapt to 3 （任憑; 由著） let （sb do as he li...
  • 機 : machineengine
  • 遺 : 遺動詞[書面語] （贈與） offer as a gift; make a present of sth : 遺之千金 present sb with a gener...
  • 傳 : 傳名詞1 （解釋經文的著作） commentaries on classics 2 （傳記） biography 3 （敘述歷史故事的作品）...
  • 過 : 過Ⅰ動詞[口語] （超越） go beyond the limit; undue; excessiveⅡ名詞（姓氏） a surname
  • 程 : 名詞1 （規章; 法式） rule; regulation 2 （進度; 程序） order; procedure 3 （路途; 一段路） journe...
  • 隨機 : random stochasticrandom
  • 遺傳 : [生物學] heredity; hereditary; inheritance; inherit
  • 過程 : process; procedure; transversion; plication; course
  1. Hidden markov models have been widely used for modeling sequences of weakly dependent random variables, with applications in as such as speech processing, neurophysiology and biology

    隱馬爾可夫模型可應用於弱相依隨機變量的建模上，也可用作研究發音過程、神經生理學與生物遺傳等方面的工具。
  2. In the genetic process of reproduction, crossover and mutation of the chromosomes in this method, these operators pr, pc and pm are produced randomly within some space, the scale of population and all kinds of genetic probability are also adjusted randomly so that the diversity individuals of population is ensured. the ga of dynamic population scale passes more information of paternal chromosomes to the offspring, which is beneficial to search the global optimization or quasi - global optimization

    該方法在染色體進行繁殖、交叉、突變的遺傳過程中，在某一范圍內隨機選取p _ r ， p _ c ， p _ m ，動態調整種群規模，保證了種群個體的多樣性；選擇同父本分別進行三種遺傳過程使得父本染色體中有更多的信息傳遞給子代，這有利於搜索全域最優解或準最優解。
  3. In rsdm, binary patterns are replaced by real - valued patterns, accordingly avoiding the coding process ; the outer learning rule is replaced by regression rule, therefore the model has not only the ability of pattern recognition but the ability of function approximation. the prearrangement of the address array bases on the distribution of patterns. if the distribution of patterns is uniform. then the address array is prearranged randomly, otherwise predisposed with the theory of genetic algorithm and the pruneing measure so as to indicate the distribution of patterns and improve the network performance. non - linear function approximation, time - series prediction and handwritten numeral recognition show that the modified model is effective and feasible

    在rsdm中，以實值模式代替二值模式，避免了實值到二值的編碼過程：以回歸學習規則代替外積法，使該模型在具有識別能力的同時具有了對函數的逼近能力；地址矩陣的預置根據樣本的分佈採取不同方法，若樣本均勻分佈，則隨機預置，否則利用遺傳演算法的原理和消減措施來預置地址矩陣，使之反映樣本的分佈，改善網路的性能。
  4. With the development of computer technology, the prominent characteristics of modern science and technology are each subject " s intercrossing, penetrating and promoting. during the course of theory investigating and practice, a great of problems are about to optimization. using genetic algorithms to optimize has been the wide focus

    隨著計算機技術的發展，各學科之間相互交叉、相互滲透、相互促進是近代科學技術發展的顯著特點之一，在理論研究與實踐的過程中存在著大量與優化相關的問題，利用遺傳演算法來進行優化已成為人們普遍關注的焦點。
  5. 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
  6. Based on the modified house of quality, the chance - constrained programming model is developed to determine the optimal striving targets by stochastic simulation and genetic algorithm, and the 0 - 1 integer programming model is derived for the decision - making of reengineering objectives

    基於改進的質量屋建立了機會約束規劃模型，通過隨機類比和遺傳演算法確定最佳奮斗目標；建立了0 - 1整數規劃模型，用於經營過程重構目標的決策。
  7. This thesis suggests a process considered minimizes the population size as similar individuals occur in the fitter members of the population, which helps reduce the execution times for ga by removing the redundancy associated with the saturation effect found in the later generation. this thesis uses a method that adds dynamic penalty terms to the fitness function according to the optimal degree of solutions, so as to create a gradient toward a feasible suboptimal or even optimal solutions. on the basis of the difference of the biggest and the smallest of fitness of individual, modifying the fitness function in order to convergence is a satisfaction

    動態調節種群大小，去掉遺傳演算法在迭代後期搜索產生的過多相似個體，達到減少計算時間的目的；按照解的優劣程度給適應度函數增加一個在ga搜索過程中動態改變的可變罰函數，給搜索最優解創造一個梯度，使遺傳演算法收斂到可行的較優解或最優解；根據適應度值最大和最小個體的差修正適應度函數，使適應度函數值適中不容易造成收斂太快、局部收斂或根本不收斂而變成隨機搜索；為了避免「近親繁殖」採用競爭擇優的交叉操作；利用并行遺傳演算法的思想，提出一種自適應多子種群進化策略；提出人口汰新政策來解決類似甚至相同的個體的情況發生。
  8. To ensure all segments under construction to approach corresponding construction segmental reasonable states in both configuration and mechanics states and good behavior of structure after completion, escaping from all kinds of accidents in whole process of construction with high efficiency, safety, excellent quality of engineering achieved, the grey prediction model ngm ( 1, 1 ) was proposed to fit prediction of any raw grey series by studying grey causes and whitening results based on the normalized mapping rules ; random perturbation method of genetic algorithms was proposed to raise efficiency of forward rolling optimization ; simultaneous analysis of strength and stability was carried out to ensure safety of strength and stability

    摘要為了保證實際施工中結構各節段在構形和受力狀態兩方面逼近施工節段合理狀態，避免事故發生，確保工程高效、安全、優質，基於歸一化映射規則，研究灰因和白果，提出了適應任意灰序列的灰預測模型ngm （ 1 ， 1 ） ；研究遺傳演算法的隨機攝動法，提高向前滾動優化效率；研究強度、穩定性的同步分析技術，保證施工過程強度、穩定性安全。
  9. The coding of the variables that describe the problem is always large. in order to find the best optimal result, it needs the larger population and the longer course of optimization. that will spend much time and money, so the appropriate population size is a factor that affects the efficiency of genetic algorithm

    在應用遺傳演算法時，初始種群的產生一般是隨機的，它往往需要的編碼長度很大，導致需要很大的種群規模或者很長的進化過程才能有較好的優化效果，這將耗費大量的計算時間和費用。
  10. A genetic algorithm ( ga ) based on building block recognition was proposed, in which building block candidates were recognized in evolving process to speed up the search so as to avoid the blindness of ga random searching

    摘要提出了一種基於積木塊識別的遺傳演算法，該演算法通過對進化過程中的候選積木塊進行識別與利用來加速搜索，從而避免遺傳演算法隨機搜索的盲目性。
  11. The major tasks include : ( 1 ) expand the schema theorem for ga. the schema theorem with binary coding advanced by professor holland is expanded to limited integer, letter, floating point numbers the number of which value is limited, and their hybrid coding. ( 2 ) put forward replacing by the excellent chromosome ga ( recga ), superiority colony first ga ( scfga ) and improve the ga ; ( 3 ) make probability convergence analysis of recga using the theory of markov chain, random process ; ( 4 ) make convergence analysis of scfga using the principle of contractive mapping in functional analysis theory ; ( 5 ) design the test programs ( cap ) to resolve np problems ( course arrangement ) with gas ; based on recga, modify the arithmetic and then conduct tests

    主要有以下幾方面工作： （ 1 ）將二進制編碼遺傳演算法的模式定理擴展到由有限整數、字母或取值個數有限的浮點數編碼，或它們混合編碼的遺傳演算法范圍； （ 2 ）提出最佳個體替換策略遺傳演算法（ recga ） 、優勢群體優先策略遺傳演算法（ scfga ） ，對遺傳演算法進行改進； （ 3 ）使用隨機過程理論markov鏈對recga進行了收斂性分析； （ 4 ）使用泛函分析理論壓縮映射原理對scfga進行了收斂性分析； （ 5 ）使用遺傳演算法設計了解決np類問題（排課問題）的測試程序（ cap ） ，並根據recga對演算法進行改進並進行測試。
  12. Another is getting the approximate optimum value and optimum solution of chance - constrained programming through some certain genetic algorithm based on random simulated technology. this paper summarizes two methods of chance constrained programming

    另一種途徑是逼近方法，利用隨機模擬技術，通過一定的遺傳演算法程序，最後得到機會約束規劃問題的近似的目標函數最優值和最優解。
  13. Genetic algorithm ( ga ) is a randomized parallel search algorithm that model natural selection, the process of evolution. ga has been widely used in engineering problems

    遺傳演算法是一種模擬生物自然選擇、進化過程的隨機、并行搜索演算法，該演算法廣泛應用於解決工程技術問題。
  14. ( 2 ) stochastic theory and other correlative theories are used to analyze iga, and the immune extend population sequence formed by iga is proved to be an aperiodic irreducible ergodic markov chain. next, the global convergence of iga is proved

    2 、利用隨機過程理論及相關理論對免疫遺傳演算法進行分析，證明了由免疫遺傳摘要演算法形成的擴展免疫種群序列的強馬爾可夫性，同時還進行了不可約性、非周期性、遍歷性等性質的研究。
  15. Genetic algorithm is a highly collateral, random, self - adaptive, general and globe search algorithm, which simulates biologic evolution process. in this paper, genetic algorithm is applied to optimizing the model optimum in what is evaluated by projection pursuit algorithm

    採用遺傳演算法對于投影尋蹤方法在評價過程中涉及到的模型優化問題進行優化，遺傳演算法是模擬生物「優勝劣汰」進化過程而形成的一種高度并行、隨機和自適應的通用性全局搜索演算法，能夠處理非線性較強的優化問題。
  16. Therefore, the original optimization model is transformed into the problem of cross sectional area optimization. this paper had great research on the development of optimization algorithm by analyzing typical optimal search method, such as greedy algorithm, simulated annealing algorithm, neural network and genetic algorithm ( ga ). according to the characteristics of truss structure, we choose genetic algorithm as the solution way

    本文在研究優化演算法發展過程的基礎上，分析了典型的優化搜索方法：確定性演算法如貪婪演算法，隨機搜索演算法如模擬退火演算法，人工智慧演算法如神經網路及遺傳演算法，根據桁架結構優化的特點，最終選擇以遺傳演算法作為桁架結構優化設計的主要演算法。
  17. The genetic algorithm is a random global search algorithm, which imitating the mechanism of living creature evolving and the nature choosing

    遺傳演算法是人們模擬生物進化過程中自然選擇和自然遺傳機制的隨機全局優化搜索演算法。
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