fitness function 中文意思是什麼
fitness function
解釋
適應度函數-
Taking gobang as an example, this paper proposes the base scheme for solve game problem by adopting the notion of genetic algorithms instead of search tree, and makes some exploratory work on the design of fitness function, it also provides with requirements for designing fitness function
以五子棋為例，提出了用遺傳演算法代替搜索樹法解決博弈問題的基本方案，並對適應度函數的設計作了一些有益地探索，給出了設計適應度函數應滿足的必要條件。 -
In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local
本文首先對聚類分析的遺傳演算法進行了研究，討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法，分析了不同遺傳操作對聚類演算法的性能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進行了分析和研究，提出了一種基於遺傳演算法的k - means聚類改進（混合聚類演算法） ，在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ，用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心，最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進行聚類。由於遺傳演算法是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用的能力，所以新的改進演算法具有較強的穩健性，可避免陷入局部最優，大大提高聚類效果。 -
Firstly, in this thesis, we discuss the development of one - dimensional cutting stock problems and other well - known algorithms about them, summarize the basic principle of genetic algorithms, and analyze the effect of coding, fitness function, crossover operators and mutation operators in the genetic process of genetic algorithms
本文首先介紹了一維下料問題的研究概況及其已有的著名演算法，綜述了遺傳演算法的基本原理和方法，分析了遺傳演算法的編碼、適應度函數、交叉和變異運算元在整個遺傳演算法的運算過程中的作用。 -
Flowing and precipitation of the self - purification is applied in the intrusion detection for optimizing the detection model and eliminating the latent intrusion data. by making fitness function, model self - purifying is realized
根據水體自凈的思想中的水體流動和沉澱的機理應用到入侵檢測演算法當中，實現了入侵檢測模型的優化，剔除了模型中潛在的入侵數據。 -
During the work of designing the structure of parallel machine tool, in allusion to the main shortcoming of stewart platform mat its workspace is strait, we decide to design a stewart type parallel machine tool which fulfill the given spherical position workspace and have the best dexterity, genetic algorithm is used in this paper. because the calculation of the fitness function is complex, backpropagation arithmetic is used to approach fitness function. the results show that mis method has good accuracy and computing efficiency
在並聯機床的結構設計中，針對並聯機床的工作空間有限且受刀具姿態影響大的弱點，提出了以保證非零最小可達章動角球形主工作空間的基礎上，使操作靈敏度暨剛度分佈最優化的設計目標，優化方法的選用了遺傳演算法，結合神經網路技術，該方法取得了很好的結果。 -
The author expatiated on the basic structure, coding manners, decoding rules, fitness function selection, self - adapted mutation and crossover operator, the judging flow of chromosome feasibility of the algorithm, finally, put forward the computing result with pattern of data table and gantt graph
詳細地闡述了演算法的基本結構、編碼方式、解碼規則、適值函數的選取、自適應變異和交叉運算元的設計、染色體可行性的判斷流程，最後以數據表和甘特圖的方式給出了計算結果。 -
Sofm neural networks is embedded into evolutionary strategy ( es ). fitness function is constructed based on the state of sofm neural networks. the sensitivity of sofm neural networks to initial weight matrix and sequence of input exemplars is overcome by the strong global optimum of es
將sofm網路嵌入到進化策略（ es ）中，根據sofm網路的運行狀態構造es的適應性函數，利用es的強搜索能力，克服sofm網路聚類效果受輸入模式次序和網路初始連接權矩陣的影響。 -
In this paper, negative selection algorithm for artificial immune system ( ais ) is improved by adopting genetic algorithm, in which euclidean distance is selected as fitness function, to generate detectors ( antibodies )
摘要將人工免疫系統的反面選擇演算法作了改進，在產生檢測器時，利用了遺傳演算法，並將歐幾里德距離作為遺傳演算法中的適應函數。 -
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。 -
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。 -
During the development, problems of actual layout have been considered, both the method using the experiential value of fitness function to identify the number of the initial rectangle parts and the method of magnifying rectangle parts to eliminate the influence of cutting gap have been advanced
在開發過程中，全面考慮了實際排樣所面臨的種種問題，提出了用適應度函數經驗值來估計初始可排矩形件數目的方法；用矩形件放大的方法來消除切縫對排樣圖的影響。 -
A new encoding strategy is designed, according to the encoding strategy, such genetic operation as population initialization, fitness function, selection, crossover, mutation are studied in detail
採用了新的編碼策略，根據這一編碼策略對種群初始化，適配值的計算，選擇，交叉，變異等遺傳操作過程進行了詳細說明。 -
A modified genetic algorithm is proposed. in the dissertation a different fitness function is constructed and similarity is introduced, which can increase variety of population md avoid prematurity
這樣可以增加群體多樣性，在一定程度上避免早熟現象發生；然後利用改進遺傳演算法對圖像進行了分割計算，將目標與背景分割開來。 -
After a short - term load forecasting method based analogous and linear extrapolation is proposed, the load forecast and the priority of equipment action are led into static reactive power optimization. the aim function is constructed for the practical situation of power system. on the basis of traditional genetic algorithm the fitness function and the holding of population diversity are improved
在提出基於相似日和線性外推的短期負荷預測新方法的基礎上，將負荷預測和設備動作優先級引入靜態無功優化中，並結合電網實際情況，構造了實用的目標函數，對遺傳演算法的適應度函數和群體多樣性的保持進行了改進，採用鄰域搜索運算元增加遺傳演算法的局部尋優能力。 -
In this paper, how to design the fitness function, encoding method, selection method, crossover method and mutaion method are discussed in detail
實驗結果表明本文設計的遺傳演算法的編碼方法和遺傳運算元是適合問題求解的。拓撲優化的結果是令人滿意的。 -
Explaining the coding scheme, fitness function, ga operator, etc details. the effectiveness is proved by the simulation result of matlab. 3
詳細闡述了設計思想、編碼方案、適應度函數的選取、遺傳運算元的改進等細節問題，並使用matlab模擬證明該方法的有效性。 -
By the coding, all first - order rules needed to explore are mapped into the points in genetic space ; ( 2 ) the fitness function, which evaluates the quality of first - order rule. the variation of fir
（ 2 ）評判一階規則優劣的適應度函數，一階規則適應度的相應變化形成了遺傳空間高低起伏的地貌特徵； （ 3 ）由交叉和變異運算元決定的規則間的鄰接關系，描述了遺傳空間地貌的溝壑或橋梁。 -
After decomposition of fitness function and adjustment of constraint condition, scheme and detail evolutes respectively and hierarchically in various processors in local network
進行適應函數分解和約束轉換后，方案設計和細部設計分別由局域網中的不同處理機分層次地協同演化完成。 -
Genetic algorithm with dual fitness function
一個具有對偶適應度函數的遺傳演算法 -
A genetic algorithm with a dual fitness function was proposed
摘要提出一個具有對偶適應度函數的遺傳演算法。
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