1994年，美国印第安纳大学的鲁道夫·拉夫和他的同事们决定用遗传学的方法来计算进化发生逆转的概率。
In 1994, Rudolf Raff and colleagues at Indiana University in the USA decided to use genetics to put a number on the probability of evolution going into reverse.
智力可以在多大程度上被测定，关于智力，我们又能从神经学、遗传学、计算机科学和其他领域中了解多少呢？
How much of intelligence can be specified, and how much can we learn about it from neurology, genetics, computer science and other fields?
许多其他的人工智能技术也被应用到这类程序中，例如神经网络，遗传算法和协同计算。
There are many other AI techniques that can be implemented and tried in a Chess program, such as neural networks, genetic algorithms, and collaborative computing.
如果量子超级计算机开发成功，那么对于解决某些问题，遗传算法将迅速成为不仅可行而且更优越的方法。
If quantum supercomputers are ever developed, genetic algorithms will suddenly become not only feasible, but preferable as an approach to solving certain problems.
说实话，在计算机科学的一些主要分支学科的命名上就可以看出生物学的影子，譬如人工神经网络，遗传算法和进化计算法等等。
Indeed, the names of major subfields of computer science-such as artificial neural networks, genetic algorithms, and evolutionary computation-attest to the influence of biological analogies.
但是研究大型食肉动物的遗传学家，美国国家癌症研究所的斯蒂芬·奥布莱恩，制定了拯救美洲豹的计划：他利用遗传学和数学计算美洲豹的命运。
But a geneticist who studies big carnivores, Stephen o 'brien of the National Cancer Institute, devised a plan to save the panthers: he combined genetics and math to calculate the panther's fate.
随后他们反向计算出基于研究开始时每个妇女已知的10个遗传风险因子的数据的癌症发生率。
Then they retrospectively calculated a prediction of cancer risk based on each woman's data for the 10 genetic risk factors known at the outset of the study.
DNA测序仪涉及在计算机硅芯片上打上纳米级微孔，然后使DNA链通过这些微孔来读取它们包含的遗传密码信息。
The DNA sequencer involves drilling tiny nanometer-size holes through computer-like silicon chips, then passing DNA strands through them to read the information contained in their genetic code.
恩迪希望能让细胞像我们熟悉的计算机那样处理遗传信息。
Dr Endy wants to make the way that cells process genetic information more like the way that familiar computers do.
最糟糕的情形下，需要耗费的时间可能是DNA复制300,000代，这一计算来自于芝加哥大学人口遗传学家乔纳森。比查德。
In the worst case, the waiting time would be 300, 000 generations, according to a calculation by Jonathan Pritchard, a population geneticist at the University of Chicago.
最糟糕的情形下，需要耗费的时间可能是DNA复制300,000代，这一计算来自于芝加哥大学人口遗传学家乔纳森。比查德。
In the worst case, the waiting time would be 300,000 generations, according to a calculation by Jonathan Pritchard, a population geneticist at the University of Chicago.
通过对遗传密码所形成网络与Linux操作系统之间的对比，我们得以洞悉生物性和计算机程序两者的根本区别。
A comparison of the networks formed by genetic code and the Linux operating system has given insight into the fundamental differences between biological and computational programming.
这所大学现在有电子计算机、高能物理、激光、地球、物理、遥感技术、遗传工程等六个新建的专业。
This university 6 newly _established faculties, namely.electronic Computer, High Energy physics, Laser, Geo-physics, Remote Sensing, and Genetic Engineering.
在某种程度上，计算机病毒的传播方式早就表现出了与遗传算法的某些相似之处。
To some extent, virus infections already exhibit processes similar to genetic algorithms.
在计算机学习用户程序中，Hadoop已经作为处理大量GA个体的规模遗传算法的一种方法(潜在解决方案)。
In machine learning applications, Hadoop has been used as a way to scale genetic algorithms for processing large populations of GA individuals (potential solutions).
这种算法在有些地方与遗传算法或竞争算法相类似，但是计算量更小，而且源程序更简单。
It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational cost and generally only a few lines of code.
对经典的遗传算法在计算中出现的随机性问题，则采用压缩映射遗传算法使计算过程渐近收敛。
For the randomness of classical GA which appears in calculation process, the contraction mapping GA is applied to make the calculation asymptotically convergent.
没有blup类型的软件和需要的计算机驱动来运行它们，是不可能取得遗传进展的。
Without BLUP type programs and the computer power required to run them, it is not possible to get the speed of genetic progress.
遗传规划(GP)是一种关于产生问题解的计算机程序或者其他复杂结构的自动方法。
Genetic programming (GP) is an automatic method for creating a computer programming or other complex structure solve a problem.
进化计算是基于遗传学和自然演化思想的一个解决优化、搜索和学习问题的有效方法。
Evolutionary computation is an effective method to solve optimization, search and learning problems, inspired by genetics and nature evolution.
许多仿真和应用结果表明遗传算法具有计算时间长、局部搜索能力弱等缺点。
Though genetic algorithms (ga's) are regarded as highly efficient global search algorithms, they turned out in many applications and simulation calculations to be not good at local search.
实验证明，该算法不但可以有效地克服标准遗传算法的缺陷，而且计算速度、精度和算法稳定性也得到了显著提高。
The research results show that the algorithm can not only overcome the short comings of SGA effectively, but also evidently improve the computing speed, computing precision and stability.
实验证明，该算法不但可以有效地克服标准遗传算法的缺陷，而且计算速度、精度和算法稳定性也得到了显著提高。
The research results show that the algorithm can not only overcome the short comings of SGA effectively, but also evidently improve the computing speed, computing precision and stability.
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