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Nominal values |
標稱型/標稱值 |
ISBN.9781617290183 |
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Numeric values |
數值型 |
ISBN.9781617290183 |
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adaptive crossover |
自適應交叉 |
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adaptive mutation |
自適應變異 |
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allele |
等位基因 |
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arithmetic crossover |
算術交叉 |
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artificial life |
人工生命 |
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Bin Packing |
裝箱問題 |
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binary genes |
二進位制編碼基因 |
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boundary mutation |
邊界變異 |
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building block hypothesis |
基因塊假設,積木塊假設 |
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cell |
細胞 |
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character genes |
符號編碼基因 |
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chromosome |
染色體 |
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classifier system,CS |
分類器系統 |
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coarse-grained PGA |
粗粒度並行遺傳演算法 |
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coding |
個體編碼 |
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crossover |
交叉 |
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crossover operator |
交叉運算元 |
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crossover rate |
交叉概率 |
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crowding |
排擠 |
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Cultural Algorithms |
文化演算法 |
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cut operator |
切斷運算元 |
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Cycle Crossover,CX |
迴圈交叉 |
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decode |
解碼 |
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decomposition parallel approach |
分解型並行演算法 |
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deoxyribonucleic acid,DNA |
脫氧核糖核酸 |
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deterministic sampling |
確定式取樣選擇 |
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diploid |
雙倍體 |
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dominance |
顯性基因 |
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dynamic parameter encoding,DPE |
動態引數編碼 |
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Edge Recombination Crossover,EX |
邊重組交叉 |
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enumerative search |
列舉搜尋演算法 |
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epistasis |
遺傳隱匿 |
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evaluation function |
評價函式 |
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evolution |
進化 |
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Evolution Strategy,ES |
進化策略 |
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Evolution Algorithms,EA |
進化演算法 |
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Evolution Computation |
進化計算 |
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Evolution Programming,EP |
進化規劃 |
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expected value model |
期望值選擇模型 |
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fine-grained PGA |
細粒度並行遺傳演算法 |
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fitness |
適應度 |
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fitness function |
適應度函式 |
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fitness landscape |
適應度景象 |
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fitness scaling |
適應度尺度變換 |
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floating-point genes |
浮點數編碼基因 |
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frequency of mutation |
變異頻率 |
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function optimization |
函式最優化 |
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GA deceptive problem |
遺傳演算法欺騙問題 |
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Gaussian mutation |
高斯變異 |
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gene |
基因 |
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generation gap |
代溝 |
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genetic algorithms,GAs |
遺傳演算法 |
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genetic operators |
遺傳運算元 |
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genetic programming,GP |
遺傳程式設計 |
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genetics |
遺傳學 |
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genome |
基因組 |
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genotype |
基因型 |
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global searching |
全域性搜尋 |
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Gray codes |
格雷碼 |
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greedy algorithm |
貪婪演算法 |
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Hammig distance |
海明距離 |
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haploid |
單倍體 |
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heredity |
遺傳 |
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heterozygous |
雜合子 |
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heuristic method |
啟發式演算法 |
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hill-climbing search |
爬山搜尋演算法 |
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homozygous |
純合子 |
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hybrid genetic algorithm,HGA |
混合遺傳演算法 |
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hypercube |
超立方體 |
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implicit parallelism |
隱含並行性 |
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individual |
個體 |
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initial population |
初始群體 |
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inverse operator |
倒位運算元 |
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island model |
島嶼模型 |
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Knapsack problem |
揹包問題 |
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lethal gene |
致死基因 |
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linear scaling |
線性尺度變換 |
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local searching |
區域性搜尋 |
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locus |
基因座 |
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machine learning |
機器學習 |
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Markov chain |
馬爾可夫鏈 |
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massively PGA |
巨並行遺傳演算法 |
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mating |
配對 |
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mating rule |
配對規則 |
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messy GA,MGA |
凌亂遺傳演算法 |
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meta genetic algorithm |
元遺傳演算法 |
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Michigan approach Michigan |
方法 |
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migration |
移民 |
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MIMD |
多指令流多資料流 |
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minimal deceptive problem,MDP |
最小欺騙問題 |
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multi-modal optimization |
多模態最優化 |
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multi-object optimization |
多目標最優化 |
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multimodal function |
多模態函式 |
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multiparameter encoding |
多引數編碼 |
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multiple hump function |
多峰值函式 |
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multiple point crossover |
多點交叉 |
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mutation |
變異 |
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mutation operator |
變異運算元 |
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mutation rate |
變異概率 |
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neighbourhood model |
鄰居模型 |
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artificial neural network,ANN |
人工神經網路 |
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non-uniform mutation |
非均勻變異 |
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Nondeterministic Polynomial Completeness |
NP-完全 |
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object function |
目標函式 |
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off-line performance |
離線效能 |
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offspring |
子代群體 |
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on-line performance |
線上效能 |
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one-point crossover |
單點交叉 |
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optimization |
最優化 |
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Order Crossover,OX |
順序交叉 |
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overspecification |
描述過剩 |
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parallel genetic algorithm,PGA |
並行遺傳演算法 |
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parallelism |
並行性 |
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Partially Mapped Crossover,PMX |
部分對映交叉 |
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Partially Matched Crossover,PMX |
部分匹配交叉 |
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penalty function |
罰函式 |
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permutation |
排列 |
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phenotype |
表現型 |
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Pitt approach itt |
方法 |
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plant pollination model |
植物授粉模型 |
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polyploid |
多倍體 |
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population |
群體 |
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population average fitness |
群體平均適應度 |
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population diversity |
群體多樣性 |
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population size |
群體大小 |
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power law scaling |
乘冪尺度變換 |
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premature convergence |
早熟現象,早期收斂 |
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preselection |
預選擇 |
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multi-modal optimization |
多模態最優化 |
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multi-object optimization |
多目標最優化 |
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multimodal function |
多模態函式 |
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multiparameter encoding |
多引數編碼 |
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multiple hump function |
多峰值函式 |
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multiple point crossover |
多點交叉 |
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mutation |
變異 |
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mutation operator |
變異運算元 |
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mutation rate |
變異概率 |
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neighbourhood model |
鄰居模型 |
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artificial neural network,ANN |
人工神經網路 |
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niche |
小生境 |
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non-uniform mutation |
非均勻變異 |
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Nondeterministic Polynomial Completeness |
NP-完全 |
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object function |
目標函式 |
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off-line performance |
離線效能 |
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offspring |
子代群體 |
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on-line performance |
線上效能 |
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one-point crossover |
單點交叉 |
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optimization |
最優化 |
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Order Crossover,OX |
順序交叉 |
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overspecification |
描述過剩 |
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parallel genetic algorithm,PGA |
並行遺傳演算法 |
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parallelism |
並行性 |
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Partially Mapped Crossover,PMX |
部分對映交叉 |
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Partially Matched Crossover,PMX |
部分匹配交叉 |
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penalty function |
罰函式 |
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permutation |
排列 |
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phenotype |
表現型 |
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Pitt approach itt |
方法 |
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plant pollination model |
植物授粉模型 |
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polyploid |
多倍體 |
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population |
群體 |
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population average fitness |
群體平均適應度 |
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population diversity |
群體多樣性 |
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population size |
群體大小 |
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power law scaling |
乘冪尺度變換 |
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premature convergence |
早熟現象,早期收斂 |
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preselection |
預選擇 |
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probabilistic algorithms |
概率演算法 |
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probabilistic operator |
概率運算元 |
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probability of crossover |
交叉概率 |
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probability of inversion |
倒位概率 |
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probability of mutation |
變異概率 |
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proportional model |
比例選擇模型 |
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random algorithms |
隨機演算法 |
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random searching,RS |
隨機搜尋演算法 |
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random walks |
隨機遊走 |
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rank-based model |
排序選擇模型 |
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read-coded genes |
浮點數編碼基因 |
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recessive |
隱性基因 |
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remainder stochastic sampling with replacement |
無回放餘數隨機選擇 |
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reordering operator |
重排序運算元 |
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reproduction |
複製 |
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ribonucleic acid,RNA |
核糖核酸 |
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robustness |
穩健性 |
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roulette wheel selection |
賭盤選擇 |
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scaling with sigma truncation |
O~截斷尺度變換 |
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schema |
模式 |
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schema defining length |
模式定義長度 |
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schema order |
模式階 |
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Scheme Theorem |
模式定理 |
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selection |
選擇 |
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selection operator |
選擇運算元 |
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sharing function |
共享函式 |
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SIMD |
單指令流多資料流 |
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simple genetic algorithm,SGA |
基本遺傳演算法 |
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simple mutation |
基本變異 |
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simulated annealing,SA |
模擬退火演算法 |
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single hump function |
單峰值函式 |
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splice operator |
拼接運算元 |
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standard parallel approach |
標準型並行方法 |
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stepping-stone model |
踏腳石模型 |
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stochastic sampling with replacement |
無回放隨機選擇 |
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stochastic tournament model |
隨機聯賽選擇模型 |
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termination conditions |
終止條件 |
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test function |
測試函式 |
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Traveling Salesman Problem,TSP |
旅行商問題 |
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two-point crossover |
雙點交叉 |
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underspecification |
描述不足 |
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uniform crossover |
均勻交叉 |
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uniform mutation |
均勻變異 |
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X chromosome |
X 染色體 |
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Y chromosome |
Y 染色體 |
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