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