隨機搜索演算法 的英文怎麼說

中文拼音 [suíjīsōusuǒyǎnsuànfǎ]
隨機搜索演算法 英文
random searching rs
  • 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; ...
  • 演 : 動詞1 （演變; 演化） develop; evolve 2 （發揮） deduce; elaborate 3 （依照程式練習或計算） drill;...
  • 算 : Ⅰ動詞1 （計算數目） calculate; reckon; compute; figure 2 （計算進去） include; count 3 （謀劃;計...
  • 法 : Ⅰ名詞1 （由國家制定或認可的行為規則的總稱） law 2 （方法; 方式） way; method; mode; means 3 （標...
  • 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. 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。
  2. 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
  3. 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。
  4. Then navigation asteroids are selected under a integral evaluation, the planning of the photoing sequence is handled with an improved genetic algorithm, along with a difference selection method which optimizes the ratio of navigation evaluation to resource consumption. a single axis randomized expanding algorithm is proposed to solve the large angle slew maneuvers planning problem. this algorithm randomly produces

    對于自主探測器大角度機動規劃問題，本文提出單軸隨機擴展演算法，單軸隨機演算法在生成隨機節點過程中充分利用鄰近點的信息，把規劃問題構造空間的維數由3減少到2 ，從而減少問題求解的搜索空間，最後利用前向搜索的方法對規劃路徑進行優化。
  5. Genetic algorithm ( ga ) is a kind of highly paralel, stochastic, global probability search algorithm based on the evolutionism such as natural selection, genetic crossover and gene mutation

    遺傳演算法是一種基於自然選擇、遺傳雜效和基因變異等生物進化機制的高度并行、隨機、全局性概率搜索演算法。
  6. 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。
  7. 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。
  8. Genetic algorithm ( ga ) simulating biologic evolution mechanism is stochastic - like search method

    遺傳演算法是模擬生物進化機制而發展起來的隨機化搜索演算法。
  9. The research for key techniques of turbo codes is processed. it includes, ? the design of optimal component codes and the performance of asymmetric turbo codes are analyzed ; ? a search algorithm for short random interleaver based on the distance spectrum and ids criteria is carried out and simplified ; ? random puncturing method to improve the weight distribution of turbo codes with some special code rates is analyzed and simulated. ? the effect of different schemes of trellis termination to the performance of turbo codes is analyzed ; ? a new low complexity decoder structure is provided ; 5

    對turbo碼的部分關鍵問題進行分析和改進，主要包括： ?分析了最優分量碼的設計和非對稱turbo碼的性能； ?設計了基於距離譜和ids的短隨機交織器搜索演算法並進行了簡化； ?提出了採用隨機刪余方式改善特定高碼率turbo碼重量分佈特性的方法； ?分析了不同編碼器狀態歸零方案對turbo碼性能的影響； ?提出了一種降低實現復雜性的turbo迭代譯碼器結構。
  10. 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。
  11. 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。
  12. The structure and mechanism of jit compiler is described as sections below : basic block splitting static and dynamic basic block splitting algorithm is described here, and optimized “ n look forward ” algorithm is proposed

    隨後分為如下幾部分細描述了編譯內核的實現機制與運行過程:基本塊的劃分描述了靜態和動態的基本塊劃分演算法，並提出效率上更加優化的「 n級預先搜索」演算法。
  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. This paper improves some commonly used stochastic optimization algorithms, such as genetic algorithm, simulated annealing algorithm and tabu search algorithm, and improved algorithms are verified by the standard mathematical functions. then, improved algorithms are used to solve practical electromagnetic problems and the items of practical application of algorithms worthy of paying attention to are emphasized

    本文對遺傳演算法、模擬退火演算法、禁忌搜索演算法等幾種常用的隨機類優化演算法進行了改進，以標準數學函數作為檢驗依據對改進演算法作出評價，並將各改進的演算法應用於實際電磁場逆問題的求解，總結其應用的實際經驗和見解。
  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。
  18. In recent years, some non - linear stochastic optimistic algorithms are developed by biology, physics, artificial intelligence and nonlinear science, such as genetic algorithm, simulated annealing, tabu searching and chaos searching et al

    近年來，基於生物學、物理學、人工智慧和一些非線性科學而發展了一些具有全局優化性能且通用性強的隨機搜索演算法，如：遺傳演算法、模擬退火、禁忌搜索和混沌搜索等。
  19. Genetic algorithm ( ga ), developed by american professor j. holland, is a sort of randomly searching algorithms which originated from the creature rule - nature selection and genetic mechanism, and has a main characteristic which is that the group searching strategy and the switching and searching for message between individuals are independent of gradient information

    遺傳演算法由美國教授j . holland提出，它是一類起源於生物選擇和遺傳機制的隨機搜索演算法。遺傳演算法的主要特點是群搜索策略以及個體間信息交換和搜索不依賴梯度信息，它特別適合於傳統方法難于解決的復雜和非線性問題並廣泛應用於機器學習、自適應控制、組合設計、人工智慧等領域。
  20. In this paper we study the key technology to the implementation of genetic algorithm and put forward a particular scheme of how to determine the parameters and operations when classify, including individual encoding, fitness function design, ga operators design, etc. thus, we give the method of how to mining classification rules using ga in theory

    遺傳演算法是一種基於生物進化論和分子遺傳學的全局隨機搜索演算法。本論文對應用遺傳演算法實現分類規則挖掘的關鍵技術進行了分析，包括個體編碼、適應度函數的設計、遺傳操作運算元的設計等，從理論上闡述了基於遺傳演算法的分類規則挖掘的方法。
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