似親群體 的英文怎麼說
中文拼音 [sìqīnqúntǐ]
似親群體
英文
autocolong-
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。
分享友人