隨機選擇演算法 的英文怎麼說

中文拼音 [suíjīxuǎnzháiyǎnsuànfǎ]
隨機選擇演算法 英文
stochastic selection algorithm
  • 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
  • 選 : Ⅰ動詞1. （挑選） select; choose; pick 2. （選舉） elect Ⅱ名詞（挑選出來編在一起的作品） selections; anthology
  • 擇 : 擇動詞（挑選） select; pick; choose
  • 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
  • 選擇 : select; choose; opt; election; choice; culling; alternative
  1. We have improved the reserves valuation method with several interest models. it uses a new mathematical method to analyze the reserves of the risk caused by the fluctuations in interest rate, and enables a new way to the risk managements of interest rates in insurance companies

    本文通過對傳統條件下準備金提留演算法進行改進，以及選擇適當的利率模型，實現了montecarlo模擬在準備金分析上的實證應用，提出了在隨機利率條件下，利率風險準備金提留的一種比較新穎的數理方法。
  2. 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。
  3. It takes use of pseudo - random technology, dynamic adaptive technology, multi - channel technology, random position embed technology and so no. so the digital watermarking can resist the physical process of printing and scanning. at the same time, the watermark is binary image which includes a great deal of information, such as personal id, secret information, even a piece of map

    本文特別針對印刷和掃描給數字圖像帶來的誤差的問題，設計了一種新的水印演算法，綜合運用了偽隨機處理技術、動態自適應技術、不同應用選擇不用頻帶處理技術、隨機位置嵌入技術、多通道嵌入等技術，使得本文所設計的水印演算法能夠抵抗印刷和掃描的物理轉換過程，同時本文所設計的水印是二值圖像，能夠承載大量的信息，例如個人id 、機密信息、商標標識，甚至可以是一幅地圖。
  4. Firstly, we introduced the main idea, the formalized description, and the basic flow of co - evolution algorithm. then, from the point of pattern analyzation, we established the mathematics model of the multi - population co - evolution algorithm based on pattern replicator equation of the single population genetic algorithm, and made the theoretical analysis and compare for the method of best choice and the method of random choice of the co - evolution algorithm. we put forward a new method for the individual fitness evaluation, and validated the performance of the new method by the simulation experiment

    首先，在介紹了協進化演算法的核心思想、形式化描述和基本演算法流程的基礎上，從模式分析角度出發，建立了基於模式復制方程的多群體協進化演算法數學模型，對協進化演算法中的最優選擇法和隨機選擇法進行了理論分析與比較，提出了一種新的個體適應度評價方法，並通過模擬實驗驗證了新方法的效率。
  5. 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。
  6. 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。
  7. 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。
  8. 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
  9. 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。
  10. The search space is divided into many small areas, and each area is given a certain pheromone value. according to the state transition rules, the artificial ants move to the next solution which is generated randomly or calculated by particle swarm optimization. local search strategy is also added into psaco so that the search speed and precision is enhanced

    該演算法首先將連續對象定義域平均分成許多邊緣相互重疊的小區域，區域的稠密程度決定了演算法解的精度，每個區域賦予一定的信息素值;螞蟻根據狀態轉移規則在隨機生成的可行解與利用微粒群演算法得出的可行解之間選擇下一步要去的位置;引入局部尋優策略，加強近似最優解鄰域內的局部搜索，提高搜索速度和精度。
  11. This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and markov chains

    這課程研究如何用亂數並透過隨意抽樣、隨機選擇證物、破壞對稱以及馬可夫鏈使得演演算法更簡單和更有效率。
  12. The main achievements are as follows : according to the stochastic mechanism, a novel ant colony optimization algorithm with random perturbation behavior ( rpaco ) is presented in this paper. the new algorithm includes two important aspects : a amplify factor formulated by inverse exponent function is developed, which is used to avoid premature, on the other hand, corresponding transition strategy with random selection and perturbation behavior is designed, which is designed to prevent the algorithm from stagnating

    主要成果有：從隨機優化技術出發，提出了一種新穎的隨機擾動蟻群優化演算法，該演算法包含了兩個重要方面：一是提出了採用倒指數曲線來描述的放大因子，可以有效地防止早熟；二是設計出了相應的隨機選擇策略和擾動策略，避免演算法陷入停滯。
  13. The selection of the pn sequence is very important because it influences the quality of the channel measurement directly. so it is discussed in this paper. because this system is a set of software radio, that is to say that the system is based on the pc platform and all other function is implemented by programming, so the selection of correlation fast algorithm, which is used in receiver frequently, is also very important

    本文對直序擴頻技術進行了系統地研究，對其中偽隨機碼的選擇直接影響到通道測量的質量的問題進行了討論，因為本系統是一套軟體無線電，即以pc機為平臺，其他的功能都由軟體編程完成，所以對信號接收中大量使用的相關的快速演算法的選擇也至關重要。
  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. In order to make the user interface agent adjust to evolvement of the system, the dissertation also proposes a collaborator selection method - multi - level top - n random selection algorithm that ensures the higher adaptability and flexibility. we study the negotiation in multi - agent system. our research work deals with two different methods of forming cooperative relation

    為了使用戶介面agent具有適應系統變化的能力，本文提出了一種選擇合作者的演算法? ?多級top - n隨機選擇演算法，該演算法兼顧了合作者完成任務的歷史情況和能力變化等因素，保證了系統具有較好的適應性和靈活性。
  16. 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。
  17. 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。
  18. In our simulation experiment in aglet, stochastic selecting method takes more time than ann method to prove our suppose correct. by learning agent can obtain a more superior result and get the basi s for reasonable choice host. this paper is organized as follow

    通過人工神經網路學習后的agent執行時間比隨機選擇時間少，並在aglet平臺對此演算法進行了模擬實驗，通過學習的agent能獲得較優的結果，為合理的選擇host提供依據。
  19. 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。
  20. 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。
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