隨機搜索 的英文怎麼說

中文拼音 [suíjīsōusuǒ]
隨機搜索 英文
random searching
  • 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
  • 搜 : 動詞1. （尋找） collect; gather2. （搜查） search; ransack
  • 索 : Ⅰ名詞1 （大繩子; 大鏈子） a large rope 2 （姓氏） a surname Ⅱ動詞1 （搜尋; 尋找） search 2 （要; ...
  • 隨機 : random stochasticrandom
  • 搜索 : 1 （仔細尋找） search for; ferret about; hunt for; scout around 2 [電子學] hunting; scan; [控] in...
  1. Simulated annealing algorithm is a kind of heuristic monte carlo method. in the course of solve the optimum problem, it conquer the blindfold searching mechanism of normal monte carlo method, and based on a appointed theory to guides search, so it can ensure a successful search

    模擬退火方法是一種啟發式蒙特卡羅法，在解決優化的問題中，它克服了常規蒙特卡羅方法盲目的隨機搜索機制，而是在一定的理論指導下搜索，故能保證搜索成功。
  2. Aimed at multiple - limit, multiple - object, non - linear, discrete of voltage / var optimization and control, on account of whole evolution of evolutionary programming, no demand for differentiability of optimal function, and random search, it can obtain global optimum with mayor probability, this paper solve optimal function with evolutionary programming

    在對優化的具體實現過程中，由於進化規劃著眼于整個整體的進化，對于所求解的優化問題無可微性要求，採用隨機搜索技術，能以較大的概率求解全局最優解的特點，針對電壓無功控制模型是一個多限制、多目標、非線性、離散的優化控制問題，因此應用進化規劃演算法進行模型的求解。
  3. Considering the one - sidedness and inaccuracy of knowledge discovery only from single - color database, an approach is proposed to discover knowledge from 1331 groups of mix - color database with partial least - square regression, based on measuring and learning 400 groups of single - color database. by this method, the mean error decreases when converting from rgb to cmyk, the precision of color matching is improved, and the automatic and general problem in color matching is further solved

    本文基於統計學習理論構造了一種快速自適應隨機搜索演算法，證明了演算法的收斂性.給出了一種簡易實用的寬帶天線匹配設計新方法.應用該自適應演算法進行天線匹配設計，不僅演算法簡單，易於編程實現;而且能夠快速設計出具有較好性能的匹配網路，非常適用於各種短波、超短波天線的匹配設計問題
  4. According to the geometrical characteristic of the arch dam shape, and based on the parameter design language of ansys software ( apdl ), the subp method and the stochastic search method are adopted together to optimize design of hyperbolic arch dam with variable thicknesses of the circle

    摘要根據拱壩體形的幾何特徵，基於ansys軟體的參數化設計語言（ apdl ） ，將零階近似方法和隨機搜索法結合起來對單圓心變厚度雙曲拱壩進行了優化設計。
  5. Genetic algorithms derives inspiration from the natural optimization process. the " survival of fittest " is applied to the population

    遺傳演算法ga （ geneticalgorithms ）是一種模仿生物界自然選擇原理和自然遺傳機制的隨機搜索最優演算法。
  6. Because ga possesses the traits of can global random search, the robustness is strong, been use briefly and broadly, it didn ’ t use path search, and use probability search, didn ’ t care inherence rule of problem itself, can search the global optimum points effectively and rapidly in great vector space of complicated, many peak values, cannot differentiable. so it can offset the shortages of nn study algorithm, can reduce the possibility that the minimum value get into local greatly, the speed of convergence can improve, interpolation time shorten greatly, the quantity of training reduce

    因為遺傳演算法具有全局隨機搜索能力，魯棒性強、使用簡單和廣泛的特點，它不採用路徑搜索，而採用概率搜索，不用關心問題本身的內在規律，能夠在復雜的、多峰值的、不可微的大矢量空間中迅速有效地尋找到全局最優解，所以可以彌補神經網路學習演算法的不足，使陷入局部最小值的可能性大大減少，使得收斂速度提高，訓練量減小。
  7. Adaptive mutation algorithm has been adopted to ensure the global random searching speciality for feasible individual

    而對可行個體，則採用自適應變異演算法，以保證演算法的全局性隨機搜索特性。
  8. Genetic algorithm is a random searching method which simulates natural selection and evolution. this method has some advantages that other usual methods do n ' t have because of its two characters - - - - - - implicit parallelism and global searching

    遺傳演算法是模仿自然選擇與進化的隨機搜索方法，由於其隱含并行性和全局搜索特性，使其具有其他常規優化演算法無法擁有的優點。
  9. Recently years, there is a new optimization method named genetic algorithms ( ga ) which is based on the numbers of genus groups. this method is a kind of random searching method which simulated natural selection and evolution. compared with traditional optimization method, genetic algorithms has two notable characters. one character is latent parallel and the other is seaching in the whole area. and genetic algorithms has some advantage which traditional method do n ' t have, for example, in genetic algorithms we did n ' t need the calculation of grade

    遺傳演算法[ geneticalgorithms ，簡稱ga ]是近些年來出現的一種模仿自然選擇與進化的基於種群數目的隨機搜索演算法，是優化領域的一個新成員。與常規優化演算法相比，遺傳演算法具有隱含并行性和全局搜索特性這兩大顯著特徵，並具有一些常規優化演算法所無法擁有的優點，如不需梯度運算等。
  10. The reason why we integrate them is that k - means algorithm is a mountain climbing method, which is easy convergent to local extremum, and sensitive to the original condition, but its convergent speed is relatively fast, and that genetic algorithm is a random searching method, which can find the whole extremum in a rather big probability, and non - sensitive to the original condition, but its convergent speed relatively slow

    之所以將：二者結合在一起，是回為k一均值演算法是一種爬山法，容易收斂到岡部極小值，對初始條件較敏感，但收斂速度較快，而遺傳演算法是卞dl隨機搜索演算法，能夠以較大概率找到全局最憂解，且對們始條件個敏感，但收斂速度較慢。
  11. Finally, some methods automatically select the optimal location of distribution substations without candidate substation location. and they may be optimal in a short term. but the substation location ca n ' t be optimal in a long range unless it is directed by the long term distribution planning

    最後，在變電所選址過程中，雖然有些演算法不需要候選所址，進行隨機搜索確定變電所位置，但它們未考慮選址過程中電力規劃專家的選擇和判斷所起的決定性作用，很難實現電力系統的長期最優。
  12. Based on the global stochastic searching method of classic genetic algorithm ( ga ), and using the diversity preservation strategy of antibodies in biology immunity mechanism, the method greatly improves the colony diversity of ga and has better global searching capability

    該演算法在傳統遺傳演算法全局隨機搜索的基礎上，借鑒了生物免疫機制中抗體的多樣性保持策略，改善了遺傳演算法的群體多樣性，具有更好的全局搜索能力。
  13. The non - linear inversion methods are developed to overcome the difficulties met when the linear inversion methods are used in the geophysical inversion problems. this paper presents the simulated annealing is hopeful for the inversion problem of resistivity map reconstruction. due to the slow speed caused by the global searching, so for, the method is limited in the processing the 1 - d model

    為了克服像傳統線性反演方法中解易陷入局部極小的不足，本文採用了模擬退火這種非線性方法實現了高密度電法一維反演，初步取得到較好反演結果，但由於它隨機搜索全局解空間，計算量非常大，因此目前主要應用於一維地電結構的反演。
  14. Evolutionary computation including genetic algorithms, evolutionary progranuning, evolution strategies and genetic programming, is a class of stochastic search algorithms

    進化演算法是借鑒生物自然選擇和遺傳機制而產生的隨機搜索演算法，主要包括遺傳演算法、進化規劃、進化策略、遺傳編程。
  15. 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。
  16. 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。
  17. Genetic algorithm ( ga ) is a high - effective randomly searching algorithm, based on the nature evolution. it is a very effective algorithm to resolve np - completed combination optimization problem

    遺傳演算法是一種借鑒于生物界自然選擇和進化機制發展起來的高度并行、自適應的隨機搜索演算法，是一種非常有效的解決np完全的組合問題的方法。
  18. Genetic algorithm is a kind of stochastic whole - searching regression algorithm, which is built on natural selection and molecule genetic mechanism, as a kind of universal algorithm to optimize the problems of complicated system, it is widely used in many fields due to its suppleness, universality, well self - fitness, robustness and fitness for collateral process, as a kind of bionic algorithms, the research on ga ' s application keeps far ahead of its theoretic research

    遺傳演算法是藉助生物界自然選擇和遺傳學機理而建立的一種迭代全局優化隨機搜索演算法，是一種求解復雜系統優化問題的通用框架。它不依賴于問題的具體領域，具有簡單、通用、較強的自適應性和魯棒性，以及適于并行處理等顯著特點，因此被廣泛應用於眾多領域。作為一種仿生演算法，遺傳演算法的應用研究遠遠領先於演算法的基礎理論研究。
  19. In the algorithm, a real - coded genetic algorithm ( rga ) is firstly adopted to globally search for the optimal solution to the optimization problem for some steps, and then a specially designed local optimization algorithm named modifiable search space random search algorithm is adopted to intensify the search for the optimal solution in the local region determined by the rga so as to improve the search efficiency and the solution quality

    該演算法首先採用實值編碼遺傳演算法對優化問題進行全局最優搜索若干步，在此基礎上，採用一種專門設計的變搜索空間隨機搜索局部優化演算法加強對重點區域的搜索，以提高搜索效率和改善解的質量。此外，為了提高遺傳演算法的搜索效率，改進了一種自適應交叉概率和變異概率的計算方法。
  20. Traditional optimization techniques search for the best solutions using gradients or random searching

    傳統的最優化技術大多是基於梯度尋優技術或隨機搜索的方法。
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