probability of mutation 中文意思是什麼

probability of mutation 解釋
變異概率
  • probability : n 1 或有；或然性。2 【哲學】蓋然性〈在 certainly 和 doubt 或 posibility 之間〉。3 【數學】幾率，...
  • of : OF ＝Old French 古法語。
  • mutation : n. 1. 變化,變異,更換；【生物學】突變；突變種；【語言學】母音變化；【音樂】（提琴的）變換把位；變聲；【法律】讓受。2. （人世的）浮沉,盛衰。
  1. In the genetic process of reproduction, crossover and mutation of the chromosomes in this method, these operators pr, pc and pm are produced randomly within some space, the scale of population and all kinds of genetic probability are also adjusted randomly so that the diversity individuals of population is ensured. the ga of dynamic population scale passes more information of paternal chromosomes to the offspring, which is beneficial to search the global optimization or quasi - global optimization

    該方法在染色體進行繁殖、交叉、突變的遺傳過程中，在某一范圍內隨機選取p _ r ， p _ c ， p _ m ，動態調整種群規模，保證了種群個體的多樣性；選擇同父本分別進行三種遺傳過程使得父本染色體中有更多的信息傳遞給子代，這有利於搜索全域最優解或準最優解。
  2. Simple genetic algorithm gets local minimization too easily and converges slowly. to solve these problems, adaptive crossover rate that has reverse hyperbolic rel ation with the numbers of iteration is designed, and adaptive mutation rate that has reverse proportion to the distances of parents and reverse exponential relat ion to the numbers of iteration is put forward. the practical simulation results show that the adaptive ga has greater convergence speed and larger probability o f getting the best solution

    簡單遺傳演算法存在著收斂速度慢、易陷入局部極小等缺陷.針對這些缺陷，本文設計出隨相對遺傳代數呈雙曲線下降的自適應交換率，並提出與父串間的相對歐氏距離成反比、隨相對遺傳代數指數下降的自適應變異率.實例驗證表明，具有自適應交換率和變異率的遺傳演算法在收斂速度和獲得全局最優解的概率兩個方面都有很大的提高
  3. Its encoding way is also analyzed in this paper. we adopt sa to produce the initial packing, which ensure the parent generations are choiceness. the crossover ( pc ) can prevent the fitness individual to be abandoned, the probability of mutation ( pm ) can prevent the algorithm is convergent before premature

    文中對其編碼方式進行分析，採用模擬退火法產生初始布局，保證了父輩解群的優良性，採用交叉概率pc有效地防止具有高適應度值的個體被排擠掉，變異概率pm防止了搜索在成熟前收斂。
  4. In this paper, fuzzy pid controller based on t - s model has been studied. due to lacks of criterion of optimization and excessive tuning parameters, the adaptive genetic algorithm with variable cross and mutation probability is used to optimize the parameters and the performance of control systems is improved. firstly, based on modified pid - flc with four fuzzy rules, scaling factor and the fuzzy consequent parameters are optimized by aga with multiple performance indexes respectively

    本文主要研究基於t - s模糊模型構成推理形式的模糊pid控制器，針對以往的模糊pid控制沒有統一的參數整定的準則及大量的待整定參數，本文採用具有動態交叉、變異概率的自適應遺傳演算法（ aga ）優化控制器的待定參數，改善了系統的控制性能。
  5. The theory and the implementation of the genetic algorithms are discussed in detail. the question on how to choose the crossover probability, the mutation probability, the scale of population and the numbers of the generation is discussed. then, the mathematics model of the optimal design is established

    詳細介紹了遺傳演算法的理論和實現技術，探討了交叉概率、變異概率、群體規模、進化代數等變量的選取問題，建立起了基於遺傳演算法的深基坑支護結構設計的優化模型，結合彈性地基梁有限元法，利用fortran語言編制了gafortran優化程序，程序中包括普通遺傳演算法和改進遺傳演算法。
  6. 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。
  7. Based on the genetic algorithm ' s global searching capability with probability regulation and euclid ' s space distance metric to settle multi - objective, the algorithm that integrates multi - objective ' s decision - making into the modified genetic algorithm to solute the optimal model with discrete variables and multi - objective is proposed. during the algorithm ' s design, the euclid ' s space distance metric is proposed to transform the multi - objective problem into single objective problem. and some modified measure to fitness function and crossover probability and mutation probability are used to improve the performance of the algorithm and avoid premature convergence

    演算法設計過程中，利用歐幾里德空間距離準則和罰函數法，將含有約束條件的多目標規劃問題轉化為無約束的單目標優化問題；針對簡單遺傳演算法出現的早熟，構造隨進化代數動態調整適應度的適應度函數和隨個體適應度自適應調整的交叉、變異概率；提出比例選擇與精英保留策略相結合的選擇、兩點交叉和簡單變異的改進遺傳演算法。
  8. Better segmentation effect can be attained by coding gray levels of pixels as eigenvector, taking advantage of histogram entropy principles function as fitness function, adopting ranking selection operation, making use of arithmetic crossover and mutation at a certain probability, combining with clustering analysis to initialize clustering center of the population to segment cells image with genetic algorithm

    以像素的灰度值為特徵向量進行編碼，利用直方圖熵法準則函數作為適應度函數，採用基於排名的選擇操作，以一定的概率進行算術交叉和變異，並結合聚類分析設定種群的聚類中心對細胞圖像進行遺傳聚類分割。
  9. This dissertation defines two parameters based on the diversity measure as the input of the flc which is used to control the ga ' s crossover probability and mutation probability

    本文從群體多樣性的角度出發，定義了兩個參數作為模糊邏輯控制器的輸入變量，輸出為遺傳演算法的交叉概率和變異概率。
  10. Based on the analysis of the influence of ga operators on population diversity, this paper studies parameter control of ga and proposes a self - adaptive mutation probability. it can automatically regulate the mutation probabilities according to the population diversity at primitive stage and adopt different mutation probabilities at different stages. the results show that it is better than the fix and adaptive mutation probabilities

    在分析遺傳運算元對種群多樣性影響的基礎上，研究了ga的參數控制問題，提出了一種自適應性變異概率，它可在ga進化初期隨著種群多樣性的變化而自適應調整變異概率，並在ga進化的不同階段採用不同的變異概率。
  11. ( 3 ) the span analysis of the genetic operators. 8 schemes were provided to analyze the relation of the genetic operators such as population size, cross probability, mutation probability. at last, a optimum group of genetic operators was selected

    提出8種方案來分析遺傳操作運算元（種群規模、交叉概率和變異概率）之間的聯系，並根據精度最高原則最終確定一組合適的遺傳操作運算元。
  12. ( a ) chaos initialization is adopted and subpopulations are classified as several types according to the values of crossover and mutation probability

    利用混沌思想產生初始群體，並依交叉和變異概率值對子群體進行分類； b
  13. The probability to obtain the correct answer of genetic algorithm is big through the use of optimized crossover and mutation operators

    本文給出了對圖像拼接問題進行了優化的交叉和變異運算元，這樣使得遺傳演算法能夠以非常大的概率獲得正確解。
  14. 2. ga optimized bp neural network first, selected function for evaluating. second, used matlab toolbox to design ga ( chosen selection methods, crossover type, and mutation probability ). ga could get rid of redundant node and branch effectively from bp network, and optimized it

    （ 2 ）遺傳演算法優化網路首先確定評估函數，再利用matlab提供的遺傳演算法工具箱進行演算法設計（確定選擇方法、交叉類型、變異概率等） ，剔除網路冗餘節點和分支，實現對bp網路的優化。
  15. Selection of mutation probability of floating - point number code genetic algorithm

    浮點數編碼遺傳演算法變異概率的選取
  16. The improvements in this thesis include the hybrid code method, the method of generation of initial populations, substituting the children for parents by combination of the simulated annealing algorithm and niche, adding some new chromosomes to ensure the population ’ s diversity and using the adaptive probability of crossover and mutation

    本文的改進方案主要涉及混合編碼方式，初始種群的產生方式，採用結合模擬退火演算法和小生境思想的替代策略，迭代過程中添加新染色體，採用自適應交叉、變異概率等。
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