However, genetic learning depends upon a prediction that the future will more or less resemble the past.
然而，基因学习基于一种预测，即未来或多或少会与过去相似。
Genetic learning is learning by a species —animals of the same kind—as a whole, and it is achieved by selection of those members of each generation that happen to act in the right way.
遗传性的学习是同种类的动物作为一个整体所具备的，并在历代成员偶然的选择之下变得极其完美。
If you want to know how learning a song alters genetic patterns, affects mate choice and ultimately influences populations, you can learn that too.
如果你想知道学唱一首歌曲如何改变遗传模式，影响择偶，最终影响人口的，你也可以通过学习获得这些知识。
In machine learning applications, Hadoop has been used as a way to scale genetic algorithms for processing large populations of GA individuals (potential solutions).
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)。
But learning more about how they, and other flaws, trigger disease could lead to new drugs, genetic tests and even ways of preventing ill health.
但是通过对致病以及缺陷基因如何诱发疾病深入研究，科学家们可以研制出新型药物、基因测试方法，甚至可以解决这种“带病却健康着”的生存状况。
It is closely related with population genetic structure, environment heterogeneity and song learning.
它不仅与种群的遗传有关，而且与环境的异质性以及鸣声学习有关。
In addition, the paper makes use of Genetic Algorithms to optimize learning rates and inertia coefficients of Fuzzy-neural network, which can ensure that the controller achieves optimization control.
此外，通过遗传算法对模糊神经网络的学习速率和惯性系数等进行了优化，为控制系统实现最优控制提供了有力保证。
This paper proposes a design of the self adaptive learning fuzzy controller based on Genetic Algorithms optimization.
本文提出一种基于基因算法优化的自学习模糊控制器的设计。
Scientists are beginning to understand the genetic basis of tardigrades death-defying superpowers. And what they re learning may have profound implications for human health.
科学家开始研究这种缓步类动物的不畏惧死亡超级强权背后的基因基础。科学家的研究可能对人类的健康具有重大的意义。
The result of simulation illustrates that the signal control method based on Q-Learning is better than fixed-time control, actuated control and signal control based on genetic algorithms.
仿真实验的结果表明，基于Q -学习的信号控制方法优于定时控制、感应式控制和基于遗传算法的信号控制方法。
Genetic algorithms (GA) are optimization and machine learning algorithms inspired by processes of natural evolution.
遗传算法是由受生物进化过程启发而形成的进行优化和机器学习的算法。
Moreover, a new fast learning method of fuzzy systems both based on genetic algorithms and gradient method is proposed.
实现了一种新的基于遗传算法和梯度下降方法的快速模糊系统学习算法。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
A hybrid learning approach is presented in which genetic algorithms are used to optimize both the network architecture and the regularization coefficient.
提出了一种利用遗传算法优化前向神经网络的结构和正则项系数的混合学习算法。
Still, untangling the vast web of genetic and molecular factors involved in learning will not be easy.
解开涉及学习的遗传因素和分子因素的庞大网络仍然不容易。
Still, untangling the vast web of genetic and molecular factors involved in learning will not be easy.
解开涉及学习的遗传因素和分子因素的庞大网络仍然不容易。
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