BIOLOGICAL evolution happens by random mutation and selection.
生物进化是由随机“突变”和“选择”产生的。
The dynamic Differential Evolution (DE) algorithm with random mutation was proposed.
提出一种带有随机变异的动态差分进化算法。
Merging the parents' DNA, and then applying a random mutation to the merged DNA simulates procreation.
合并双亲的DNA，对合并后的DNA应用随机变异以模拟繁衍。
The evolvement of quasispecies is not only the result of positive immune pressure in the host, but also the accumulation of random mutation.
准种的演变不仅是机体阳性选择压力的结果，也有阴性选择的参与，即随机突变积累的结果。
Experiments reveal that the proposed algorithm behaves better than the self-organizing migrating algorithm with random mutation step and the basic one.
实验结果表明，该算法优于原始自组织迁移算法和基于随机变异步长的自组织迁移算法。
Early farmers selected out wheat that, due to a random genetic mutation, didn't shatter and was thus ideal for harvesting.
早期的农民拣出那些因随机的遗传变异而没有碎裂的小麦，由于不会碎裂，因而它们是收获的理想之选。
Neutral mutation — Evolution at the molecular level is primarily determined by mutational input and random genetic drift, rather than by natural selection.
天然突变——演化过程中在分子的层次上主要是经由随机的基因流而决定，并非借由天择所影响。
According to the character of non linear network traffic, the traffic time series is decomposed into trend component, period component, mutation component and random component.
文章考虑网络流量非线性的特点，通过不同的数学模型将流量时间序列分解成趋势成分、周期成分、突变成分和随机成分。
Some anthropologists suspect that humans of the era experienced a leap in mental abilities, fueled by random genetic mutation or the neurological nourishment of language and culture.
一些人类学家怀疑那个时代的人类在智力上可能经历过飞跃，也许是受随机基因突变的影响，或者在神经上受语言和文化的刺激。
The natural number coding, the random population selection, the simple crossover strategy of "two-parents-and-one-kid" and the fixed mutation probability were totally adopted.
在算法中采用了自然数编码、随机选取种群、简单的“双亲单子”交叉策略和固定的突变概率。
Presents mutation principles based random restart heuristics for the global optimization problem;
通过改进遗传算法，提出一种求解全局优化问题的变异基随机搜索方法。
After every basic mutation, crossover and competition, a new competition with a random swarm is added so as to effectively jump out of the local optimum and enhance the ability of global search.
在每一代变异、交叉和竞争之后，又增加了与随机新种群的竞争操作，使算法易于跳出局部最优点，以提高全局搜索能力。
After every basic mutation, crossover and competition, a new competition with a random swarm is added so as to effectively jump out of the local optimum and enhance the ability of global search.
在每一代变异、交叉和竞争之后，又增加了与随机新种群的竞争操作，使算法易于跳出局部最优点，以提高全局搜索能力。
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