進化機制 的英文怎麼說
中文拼音 [jìnhuàjīzhì]
進化機制
英文
evolutionism-
Graphical models are increasingly popular tools for modeling problems involving uncertainty. in this dissertation, they are integrated with evolutionary mechanism, to develop the evolutionary mechanism based - on graph models and more intelligent optimization theory
本文以圖形模型為線索，將圖形模型與傳統進化機理有機地結合，探索基於圖形模型的進化機制，研究更具智能性的優化理論。Furthermore, analyses of dna sequence datasets with the violation of the homogeneity assumption is not only useful to elucidate the evolutionary mechanisms that have shaped the observed differences in genes and species with atypical substitution process, but also provides important clue for the future development of phylogenetic methods
更進一步地，對于違反同質性假設的dna序列數據集的分析，不僅用於闡明在非典型替代過程下的基因和物種形成的可觀察到的差異的進化機制，也為深入研究系統發生的方法提供了重要的線索。Therefore, the study of corporate organizational structural evolution is of vital importance to comprehension and interpretation of evolution for enterprises themselves and the whole economic system. focused on the study of corporate organizational structure and based on evolutional biology, complicated systematic theory, evolutional economics and organizational ecology, this thesis elaborates on the process, influencing factors and mechanism of corporate organizational structure evolution as well as the correlations of such evolution between corporate development and enterprise ecology succession. 1. this thesis comes up with four assumptions key to the study and defines corporate organizational structure
本論文以企業組織結構進化現象作為主要研究對象，在進化生物學、復雜系統理論、進化經濟學和組織生態學等理論研究的基礎上，對企業組織結構進化的歷程、影響因素、進化機制，以及企業組織結構進化與企業成長發育和企業生態演替之間的相互關系展開了系統的討論: 1 .在提出四個關鍵的研究假設前提后，本文對企業組織結構進行了界定。Thirdly, genetic algorithm is a kind of search and optimization method simulating the life evolution mechanism, which has the advantages of global optimization and implicit concurrency
遺傳演算法是一種模擬生命進化機制的搜索和優化方法，與常規優化演算法相比，具有隱含并行性和全局搜索特性，因此選擇遺傳演算法進行尋優計算。5s rdna cloning and sequencing were carried out to analyze the 5s rdna variation pattern. genomic in situ hybridization ( gish ) was performed for better understanding of genomic relationship among closely relative pines
利用克隆測序方法對松屬植物5srdna的研究無疑是有開創性的工作，可以探討裸子植物的5srdna的進化機制和種間關系。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。There ' s two important mechanism and springboard for the research of competitive industry development and firm evolutional strategic logic. they are " evolution mechanism " and " mutation mechanism ". 2
概括起來講，本文的主要研究結論有1 、 「進化機制」 、 「突變機制」兩種機制是研究競爭性產業發展和企業戰略演進邏輯的兩個重要機制和出發點。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Combining with co - evolution mechanism, we renewedly defined the several aspect of the co - evolutionary agent, such as the belief, action, communication, decision maker, and so on. we made the co - evolutionary agent model and actual world model become a uniform closed loop system, and established the symbol deduction theory model of multi - agent system based on co - evolution mechanism
結合協進化機制，從符號演繹邏輯的角度重新定義了協進化智能體的信念、動作、通信、決策器等各個方面，將協進化智能體模型與實際世界模型形成一個統一的閉環系統，並建立了基於協進化的多智能體系統的符號演繹理論模型。( 5 ) ga ' s operators are improved to reach better evolution effects, such as choosing proper fitness function, using 2d equal - block crossover and multi - bit mutation, expending the population size to reach better evolution effects. the structural results are effectively made better
（ 5 ）本文提出了二維均勻分塊交叉、 「擴大種群」的進化機制，引入多點變異，保證了ga種群適應度平均值的有效進化和群體的多樣性，實現了拓撲結構的遺傳進化。Firstly we made an introduce for the typical evolutionary rule and strategy system, samuel. then, we made a deep analysis for the multi - agent co - evolution model based on evolutionary rule and strategy system from the point of behavior engineering, and analyzed the evaluation problem of the behavior strategy in the collaboration process of multi - agent using co - evolution mechanism
介紹了典型的進化規則策略系統samuel ，然後從行為工程的角度，對基於進化規則策略系統的多智能體協進化模型進行了深入分析，採用協進化機制分析了智能體之間協作過程中行為策略的評估問題。People devised genetic algorithm by simulating the genetic and evolution mechanism of biology
人們模仿生物的遺傳和進化機制，提出了遺傳演算法。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。Genetic algorithm is a global optimization search method, which is based on biological evolutionary mechanisms such as natural selection, heredity and mutation
摘要遺傳演算法是一種基於自然選擇和遺傳變異等生物進化機制的全局優化搜索演算法。A reliable phylogenetic inference will manifest the sequence of evolution, and facilitate our understanding of the history and mechanism of species evolution
一個可靠的系統發生的推斷，將揭示出有關生物進化過程的順序，有助於我們了解生物進化的歷史和進化機制。At the same time, ga does n ' t have to know every feature of problem, so it can accomplish solution only by genetic procedure embodying evolving mechanism
同時，遺傳演算法具有不用了解問題本身的全部特徵的特點，僅僅通過體現進化機制的演化過程來完成對問題的求解。Genetic algorithm solves problem by learning from nature and referring to evolution mechanism of creature, which has wide usage, high stability and concise, and global optimization
遺傳演算法通過向自然學習，借鑒生物進化機制求解問題，具有廣泛的可應用性和高度穩健性、簡明性和全局優化性。In order to solve this problem, a new mechanism of cooperative coevolution with local interaction for collective complex cooperation behaviors is proposed based on system theory and nonlinear science theory
針對這一問題，依據系統論和非線性科學理論，構造了一種引入局部交互的群體復雜協作行為協同進化機制。As a considerably large amount of dna sequences become increasingly available, they are widely used to study phylogeny of species and multigene families, as well as the mechanism of evolution at molecular level
基於相當大量的dna序列的獲得，其被廣泛地用於物種的系統發生、多基因家族的演化，以及在分子水平上的進化機制的研究。分享友人