遺傳操作 的英文怎麼說

中文拼音 [yízhuàncāozuò]
遺傳操作 英文
genetic manipulation
  • 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）...
  • 操 : Ⅰ動詞1 （抓在手裡; 拿; 掌握） hold; grasp 2 （做; 從事） act; do; operate 3 （用某種語言、方言說...
  • 遺傳 : [生物學] heredity; hereditary; inheritance; inherit
  • 操作 : 1 （按照一定的程序和技術要求進行活動） operate; manipulate; handle 2 （所進行的若干連續生產活動的...
  1. By whitening the covariance matrix of flat ambiguity, correlation between ambiguity components, the number of local extrema of cost function is reduced. the search program getting into precocity is avoided by suitable encoding and genetic operation

    通過白化處理模糊度，減少模糊度搜索中指標函數的局部極值點，採用適當的編碼和遺傳操作使得在種群個體較少的情況下避免搜索陷入早熟。
  2. Ethnics problem of modern genetic manipulative technology

    現代遺傳操作技術的倫理學問題
  3. Based on natural selection, it executes three same and simple genetic operators : selection, crossover and mutation. under the leading of fitness value, it makes an efficient searching in complex spaces until acquiring the best result

    它基於自然選擇的原理，通過循環執行相同的、簡單的選擇、雜交和變異三種遺傳操作，並在適應度函數值的引導下對復雜的解空間進行有效地搜索，直到獲得最優的解。
  4. It is applicable to various structural distribution networks. while resolving the " large area restoration ", the genetic algorithm execute three same and simple genetic operators : selection, crossing and mutating. it make a self - adaptable and probability overall searching under the leading of fitness value in the whole searching scale until acquiring the best result

    在求解網路故障后重構問題時，互動式模糊遺傳演算法通過循環執行相同的、極其簡單的選擇、雜交和變異三種遺傳操作，並在適應度函數值的引導下在搜索空間進行自適應概率性全局搜索，直至獲得全局最優解。
  5. This paper focuses on further development and improvement of the genetic manipulation system for a. meditteranei u32

    本論文致力於進一步發展和完善a . meditteraneiu32的遺傳操作系統。
  6. In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local

    本文首先對聚類分析的遺傳演算法進行了研究，討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法，分析了不同遺傳操作對聚類演算法的性能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進行了分析和研究，提出了一種基於遺傳演算法的k - means聚類改進（混合聚類演算法） ，在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ，用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心，最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進行聚類。由於遺傳演算法是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用的能力，所以新的改進演算法具有較強的穩健性，可避免陷入局部最優，大大提高聚類效果。
  7. This thesis presented a modified genetic operators in clustering analysis, and firstly introduced good point set - based clustering algorithm - gameans, which characterized by inferior sensitivity to initial, robustness, and removable premature, and also firstly presented a hybrid method with ga and gameans. experiment show that the hybrid method with general performances can find better clustering results

    文中討論了聚類分析的遺傳操作改進方法，首次提出了基於佳點集ga的聚類演算法gameans ，降低了傳統聚類演算法對初始化的要求，具有收斂快、較強的穩健性和可避免早熟的特點。
  8. A new encoding strategy is designed, according to the encoding strategy, such genetic operation as population initialization, fitness function, selection, crossover, mutation are studied in detail

    採用了新的編碼策略，根據這一編碼策略對種群初始化，適配值的計算，選擇，交叉，變異等遺傳操作過程進行了詳細說明。
  9. By computing valid path to evaluate individuals and cumulate local advantage modules in the procedure of genetic operations, invalid individuals can be processed and formed valid individuals at last

    通過計算個體的有效路徑，評價個體，並在遺傳操作中不斷累積局部優勢模式，可以對無效解進行遺傳操作並最終生成有效解。
  10. In order to override the well - known limitation of back propagation algorithm, such as local grade problem, we suggest genetic algorithm, a global optimization algorithm, to optimize the weights set. the different parts of this model were modularized and combined as a prediction system

    通過對固定網路結構的權系值進行遺傳操作，優化網路的權系值組合，快速收斂到最優權系值組合，進而提高網路的分析預測效率和能力。
  11. Ec employs a coding technology and some genetic operations. under the pressure of selection, which means " fits survive ", the algorithm can produce an optimal solution

    它採用簡單的編碼技術來表示各種復雜的結構，並通過對編碼進行簡單的遺傳操作和優勝劣汰的自然選擇來指導學習和確定搜索的方向。
  12. ( 3 ) the span analysis of the genetic operators. 8 schemes were provided to analyze the relation of the genetic operators such as population size, cross probability, mutation probability. at last, a optimum group of genetic operators was selected

    提出8種方案來分析遺傳操作運算元（種群規模、交叉概率和變異概率）之間的聯系，並根據精度最高原則最終確定一組合適的遺傳操作運算元。
  13. It brought forward genetic algorithms with binary character string coding, genetic operation is the best optional optimization preserving strategy operation, multipoint crossover and nonsymmetrical mutation based on researching widely the genetic algorithms " characteristics of varied coding modes, selection modes, crossover modes and mutation modes in allusion to genetic algorithms is difficult of multi - parameter coding and genetic operation ' s realization for wave impedances inversion

    針對遺傳演算法用於波阻抗反演涉及多參數編碼、以及對應遺傳操作不易實現的問題，在廣泛研究了遺傳演算法的各種編碼方式、及其對應的選擇方式、交叉方式以及變異方式特點的基礎上，提出了採用二進制字元串編碼、遺傳操作為最優保存策略選擇、多點交叉和非均勻變異的遺傳演算法。
  14. This paper brought forward global optimized wave impedances mixed inversion based on genetic algorithms with binary character string coding, genetic operation is the best optional optimization preserving strategy operation, multipoint crossover and nonsymmetrical mutation based on researching widely the genetic algorithms ' characteristics of varied coding modes, selection modes, crossover modes and mutation modes in allusion to genetic algorithms is difficult of multi - parameter coding and genetic operation ' s realization for wave impedances inversion

    摘要針對遺傳演算法用於波阻抗反演涉及多參數編碼、以及對應遺傳操作不易實現的問題，在廣泛研究了遺傳演算法的各種編碼方式、及其對應的選擇方式、交叉方式以及變異方式特點的基礎上，提出了採用二進制字元串編碼、遺傳操作為最優保存策略選擇、多點交叉和非均勻變異的遺傳演算法，基於該演算法形成了全局尋優的波阻抗混合反演方法。
  15. The physical meaning of the fuzzy neural network parameters and their influence on the model performance are discussed through the research of composite liquid - solid extrusion model, and the reasonable method of evaluating the network parameters is confirmed which can quicken the training speed. some problems and the influence of the evolution operator on the optimization course are discussed, arid the reasonable range of the evolution parameter is concluded

    通過對復合材料液-固擠壓成形工藝系統建模的研究，探討了模糊神經網路參數的物理意義及其對網路學習速度的影響，確定了可加快網路學習速度的參數賦值方法；結合液-固擠壓工藝特點探討了利用遺傳演算法進行參數優化過程中一些問題的處理方法及遺傳操作運算元對優化過程的影響，確定了參數選取的合理范圍。
  16. Standard genetic operators are not always suitable for task matching and scheduling based on permutation representation

    基於任務排列的知識表示，常規的標準遺傳操作運算元並不總是有效的。
  17. Chaos optimization search operation is introduced to simple genetic algorithm operation for mending the defect that simple genetic algorithm is premature easily

    在基本遺傳操作中引入了混沌優化搜索操作，克服了基本遺傳演算法容易「早熟」的缺陷。
  18. Considering the constraint, the method is applied, by which the invalid individual will be changed into the valid one. the optimal results are tested with the fem

    其中對于約束條件，採用違約解修正法來改進，目的是把遺傳操作中產生的不可行個體，經過修正變化成可行個體。
  19. Based on the study of genetic operations and their parameters, this paper also provides methods to develop the positive role and discard the negative one of introns

    文中通過對有關遺傳操作方法和參數的研究，定性地分析了它們對基因內區的影響，並指出抑制基因內區消極作用的途徑。
  20. There are three key technologies for the optimization : 1, chromosome spaces string is shined upon the weight coefficient ; 2, the identification of fitness ; 3, genetic operation

    用遺傳演算法優化bp神經網路的關鍵有三點: 1 、染色體位串與權系數的編碼映射; 2 、適應度函數的確定; 3 、進行遺傳操作。
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