evolutionary computing 中文意思是什麼

evolutionary computing 解釋
進化計算
  1. With the computing program, flow around the square cylinder is resolved at first, and the period of laminar flow is simulated successfully. during the period of vortex street ( re = 100 ), the periodically evolutionary phenomena of the flow behind the square cylinder can be seen

    利用計算程序，本文首先對方柱繞流流動進行求解，成功模擬了方柱繞流層流階段的流動和渦街階段（ re = 100 ）方柱尾流的周期性變化現象。
  2. The characteristics of quantum computing and the mechanism of immune evolution are analyzed and discussed. inspired by the mechanism in which immune cell can gradually accomplish affinity maturation during the self - evolution process, a immune evolutionary algorithm based on quantum computing ( mqea ) is proposed. the algorithm can find out optimal solution by the mechanism in which antibody can be clone selected, memory cells can be produced, similar antibodies can be suppressed and immune cell can be expressed as quantum bit ( q - bit ). it not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed and converge to the global optimal solution rapidly. the convergence of the mqea is proved and its superiority is shown by some simulation experiments in this paper

    分析和探討了量子計算的特點及免疫進化機制，並結合免疫系統的動力學模型和免疫細胞在自我進化中的親和度成熟機理，提出了一種基於量子計算的免疫進化演算法.該演算法使用量子比特表達染色體，通過免疫克隆、記憶細胞產生和抗體相似性抑制等進化機制可最終找出最優解，它比傳統的量子進化演算法具有更好的種群多樣性、更快的收斂速度和全局尋優能力.在此不僅從理論上證明了該演算法的收斂，而且通過模擬實驗表明了該演算法的優越性
  3. Abstract : the characteristics of quantum computing and the mechanism of immune evolution are analyzed and discussed. inspired by the mechanism in which immune cell can gradually accomplish affinity maturation during the self - evolution process, a immune evolutionary algorithm based on quantum computing ( mqea ) is proposed. the algorithm can find out optimal solution by the mechanism in which antibody can be clone selected, memory cells can be produced, similar antibodies can be suppressed and immune cell can be expressed as quantum bit ( q - bit ). it not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed and converge to the global optimal solution rapidly. the convergence of the mqea is proved and its superiority is shown by some simulation experiments in this paper

    文摘:分析和探討了量子計算的特點及免疫進化機制，並結合免疫系統的動力學模型和免疫細胞在自我進化中的親和度成熟機理，提出了一種基於量子計算的免疫進化演算法.該演算法使用量子比特表達染色體，通過免疫克隆、記憶細胞產生和抗體相似性抑制等進化機制可最終找出最優解，它比傳統的量子進化演算法具有更好的種群多樣性、更快的收斂速度和全局尋優能力.在此不僅從理論上證明了該演算法的收斂，而且通過模擬實驗表明了該演算法的優越性
  4. The innovation of the thesis as follows : advances the generalized computing theory that combines symbolic computing with neural computing, fuzzy computing and evolutionary computing

    本文的價值在於：提出了融符號計算、神經計算、模糊計算和演化計算於一體的廣義計算理論。
  5. Grid computing, most simply stated is distributed computing taken to the next evolutionary level

    簡單說來，網格是下一代具有革命性意義的計算系統技術。
  6. Here, we focus on four topics f artificiaj neural 3 : etwork ( ann ), swarm intelli - gence ( si ), evolutionary algorithjn ( ea ) and dna computing

    本文探討4個主題：人工神經元網路，群體智能，演化演算法和dna計算。
  7. Soft computing includes artificial neural network, fuzzy logic, evolutionary algorithms, rough set ( rs ) theory, etc. as a new soft computing, rough set can analyze and handle imprecise, inconsistent and incomplete data efficiently. in addition, connotative knowledge and latent rules will be discovered by using rough set theory

    粗糙集理論是一種較新的軟計算方法，它能有效地分析和處理不精確、不一致、不完整等各種不完備信息，並從中發現隱含的知識，揭示潛在的規律，是一個強大的數據分析工具，具有良好的容錯性能。
  8. In the engineering application of ci, two methods of evolutionary computing and neural computing the fourier factors are proposed which redound to the application of fourier transformation to the engineering

    在計算智能的工程應用方面，本文提出了fourier系數的進化計算和神經計算兩種智能計算方法，為fourier變換的工程應用提供了方便。
  9. Clone principle is led into evolutionary computing, and a hybrid algorithm is combining antibody clone strategy with fuzzy c - means clustering method is given. it is used in intrusion detection

    提出將人工免疫與模糊c -均值聚類技術相結合進行聚類，從而實現對異常行為的檢測的演算法。
  10. Quantum computing s premise of simultaneous evaluation of many potential solutions to a problem seems destined to fit in with genetic algorithms premise of evolutionary computing with its large sample populations

    量子計算對問題許多可能的解決方案的同步計算的這一前提，似乎註定就適應遺傳演算法用其巨大的樣本種群進行進化計算這一前提。
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