遗传算法应用程序领域有无限的潜力。
The field of genetic algorithms applications has unlimited potential.
Perl用于实现遗传算法。
病毒发生的变化类似突变，又一个遗传算法的特征。
Variations of viruses are also similar to mutations, another factor in genetic algorithms.
Perl用于实现遗传算法的主要缺点在于速度慢。
在本文中，我将介绍有关Perl遗传算法更高级的内容。
In this article, I cover more advanced material on genetic algorithms in Perl.
在冷却过程数值模拟的基础上，采用遗传算法对该问题进行求解。
On the basis of numerical simulation of cooling process, we select GA to solve the problem.
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.
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.
然后，为了加快遗传算法的收敛速度减少算法执行时间引入模拟退火机制对上述算法进行优化。
Then the mechanism of Simulated Annealing is import in the algorithm above to decrease the execution time and quickens the velocity of convergence.
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.
遗传算法模仿达尔文的自然选择，其中“适应性”选择进行生存、繁殖以及由此而来的适应性变异的个体。
Genetic algorithms mimic Darwinian natural selection, where "fitness" selects individuals for survival, breeding, and, hence, adaptive mutation.
遗传算法模仿达尔文的自然选择，其中“适应性”选择进行生存、繁殖以及由此而来的适应性变异的个体。
Genetic algorithms mimic Darwinian natural selection, where "fitness" selects individuals for survival, breeding, and, hence, adaptive mutation.
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