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Nominal values |
标称型/标称值 |
ISBN.9781617290183 |
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Numeric values |
数值型 |
ISBN.9781617290183 |
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adaptive crossover |
自适应交叉 |
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adaptive mutation |
自适应变异 |
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allele |
等位基因 |
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arithmetic crossover |
算术交叉 |
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artificial life |
人工生命 |
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Bin Packing |
装箱问题 |
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binary genes |
二进制编码基因 |
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boundary mutation |
边界变异 |
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building block hypothesis |
基因块假设,积木块假设 |
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cell |
细胞 |
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character genes |
符号编码基因 |
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chromosome |
染色体 |
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classifier system,CS |
分类器系统 |
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coarse-grained PGA |
粗粒度并行遗传算法 |
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coding |
个体编码 |
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crossover |
交叉 |
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crossover operator |
交叉算子 |
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crossover rate |
交叉概率 |
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crowding |
排挤 |
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Cultural Algorithms |
文化算法 |
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cut operator |
切断算子 |
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Cycle Crossover,CX |
循环交叉 |
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decode |
解码 |
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decomposition parallel approach |
分解型并行算法 |
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deoxyribonucleic acid,DNA |
脱氧核糖核酸 |
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deterministic sampling |
确定式采样选择 |
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diploid |
双倍体 |
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dominance |
显性基因 |
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dynamic parameter encoding,DPE |
动态参数编码 |
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Edge Recombination Crossover,EX |
边重组交叉 |
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enumerative search |
枚举搜索算法 |
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epistasis |
遗传隐匿 |
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evaluation function |
评价函数 |
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evolution |
进化 |
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Evolution Strategy,ES |
进化策略 |
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Evolution Algorithms,EA |
进化算法 |
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Evolution Computation |
进化计算 |
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Evolution Programming,EP |
进化规划 |
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expected value model |
期望值选择模型 |
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fine-grained PGA |
细粒度并行遗传算法 |
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fitness |
适应度 |
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fitness function |
适应度函数 |
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fitness landscape |
适应度景象 |
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fitness scaling |
适应度尺度变换 |
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floating-point genes |
浮点数编码基因 |
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frequency of mutation |
变异频率 |
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function optimization |
函数最优化 |
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GA deceptive problem |
遗传算法欺骗问题 |
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Gaussian mutation |
高斯变异 |
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gene |
基因 |
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generation gap |
代沟 |
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genetic algorithms,GAs |
遗传算法 |
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genetic operators |
遗传算子 |
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genetic programming,GP |
遗传编程 |
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genetics |
遗传学 |
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genome |
基因组 |
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genotype |
基因型 |
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global searching |
全局搜索 |
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Gray codes |
格雷码 |
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greedy algorithm |
贪婪算法 |
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Hammig distance |
海明距离 |
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haploid |
单倍体 |
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heredity |
遗传 |
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heterozygous |
杂合子 |
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heuristic method |
启发式算法 |
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hill-climbing search |
爬山搜索算法 |
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homozygous |
纯合子 |
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hybrid genetic algorithm,HGA |
混合遗传算法 |
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hypercube |
超立方体 |
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implicit parallelism |
隐含并行性 |
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individual |
个体 |
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initial population |
初始群体 |
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inverse operator |
倒位算子 |
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island model |
岛屿模型 |
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Knapsack problem |
背包问题 |
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lethal gene |
致死基因 |
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linear scaling |
线性尺度变换 |
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local searching |
局部搜索 |
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locus |
基因座 |
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machine learning |
机器学习 |
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Markov chain |
马尔可夫链 |
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massively PGA |
巨并行遗传算法 |
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mating |
配对 |
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mating rule |
配对规则 |
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messy GA,MGA |
凌乱遗传算法 |
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meta genetic algorithm |
元遗传算法 |
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Michigan approach Michigan |
方法 |
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migration |
移民 |
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MIMD |
多指令流多数据流 |
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minimal deceptive problem,MDP |
最小欺骗问题 |
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multi-modal optimization |
多模态最优化 |
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multi-object optimization |
多目标最优化 |
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multimodal function |
多模态函数 |
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multiparameter encoding |
多参数编码 |
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multiple hump function |
多峰值函数 |
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multiple point crossover |
多点交叉 |
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mutation |
变异 |
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mutation operator |
变异算子 |
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mutation rate |
变异概率 |
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neighbourhood model |
邻居模型 |
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artificial neural network,ANN |
人工神经网络 |
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non-uniform mutation |
非均匀变异 |
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Nondeterministic Polynomial Completeness |
NP-完全 |
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object function |
目标函数 |
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off-line performance |
离线性能 |
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offspring |
子代群体 |
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on-line performance |
在线性能 |
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one-point crossover |
单点交叉 |
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optimization |
最优化 |
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Order Crossover,OX |
顺序交叉 |
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overspecification |
描述过剩 |
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parallel genetic algorithm,PGA |
并行遗传算法 |
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parallelism |
并行性 |
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Partially Mapped Crossover,PMX |
部分映射交叉 |
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Partially Matched Crossover,PMX |
部分匹配交叉 |
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penalty function |
罚函数 |
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permutation |
排列 |
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phenotype |
表现型 |
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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 |
多倍体 |
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population |
群体 |
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population average fitness |
群体平均适应度 |
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population diversity |
群体多样性 |
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population size |
群体大小 |
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power law scaling |
乘幂尺度变换 |
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premature convergence |
早熟现象,早期收敛 |
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preselection |
预选择 |
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multi-modal optimization |
多模态最优化 |
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multi-object optimization |
多目标最优化 |
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multimodal function |
多模态函数 |
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multiparameter encoding |
多参数编码 |
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multiple hump function |
多峰值函数 |
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multiple point crossover |
多点交叉 |
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mutation |
变异 |
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mutation operator |
变异算子 |
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mutation rate |
变异概率 |
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neighbourhood model |
邻居模型 |
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artificial neural network,ANN |
人工神经网络 |
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niche |
小生境 |
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non-uniform mutation |
非均匀变异 |
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Nondeterministic Polynomial Completeness |
NP-完全 |
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object function |
目标函数 |
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off-line performance |
离线性能 |
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offspring |
子代群体 |
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on-line performance |
在线性能 |
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one-point crossover |
单点交叉 |
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optimization |
最优化 |
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Order Crossover,OX |
顺序交叉 |
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overspecification |
描述过剩 |
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parallel genetic algorithm,PGA |
并行遗传算法 |
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parallelism |
并行性 |
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Partially Mapped Crossover,PMX |
部分映射交叉 |
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Partially Matched Crossover,PMX |
部分匹配交叉 |
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penalty function |
罚函数 |
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permutation |
排列 |
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phenotype |
表现型 |
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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 |
多倍体 |
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population |
群体 |
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population average fitness |
群体平均适应度 |
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population diversity |
群体多样性 |
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population size |
群体大小 |
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power law scaling |
乘幂尺度变换 |
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premature convergence |
早熟现象,早期收敛 |
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preselection |
预选择 |
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probabilistic algorithms |
概率算法 |
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probabilistic operator |
概率算子 |
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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 |
变异概率 |
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proportional model |
比例选择模型 |
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random algorithms |
随机算法 |
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random searching,RS |
随机搜索算法 |
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random walks |
随机游走 |
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rank-based model |
排序选择模型 |
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read-coded genes |
浮点数编码基因 |
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recessive |
隐性基因 |
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remainder stochastic sampling with replacement |
无回放余数随机选择 |
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reordering operator |
重排序算子 |
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reproduction |
复制 |
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ribonucleic acid,RNA |
核糖核酸 |
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robustness |
稳健性 |
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roulette wheel selection |
赌盘选择 |
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scaling with sigma truncation |
O~截断尺度变换 |
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schema |
模式 |
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schema defining length |
模式定义长度 |
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schema order |
模式阶 |
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Scheme Theorem |
模式定理 |
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selection |
选择 |
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selection operator |
选择算子 |
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sharing function |
共享函数 |
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SIMD |
单指令流多数据流 |
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simple genetic algorithm,SGA |
基本遗传算法 |
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simple mutation |
基本变异 |
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simulated annealing,SA |
模拟退火算法 |
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single hump function |
单峰值函数 |
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splice operator |
拼接算子 |
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standard parallel approach |
标准型并行方法 |
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stepping-stone model |
踏脚石模型 |
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stochastic sampling with replacement |
无回放随机选择 |
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stochastic tournament model |
随机联赛选择模型 |
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termination conditions |
终止条件 |
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test function |
测试函数 |
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Traveling Salesman Problem,TSP |
旅行商问题 |
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two-point crossover |
双点交叉 |
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underspecification |
描述不足 |
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uniform crossover |
均匀交叉 |
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uniform mutation |
均匀变异 |
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X chromosome |
X 染色体 |
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Y chromosome |
Y 染色体 |
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