关于遗传算法的工作原理：基因组固定假说
On the Workings of Genetic Algorithms: The Genoclique Fixing Hypothesis
摘要：
我们最近报道，简单的遗传算法（SGA）是能够执行一个显着的形式的次线性计算，有一个直接的连接与一般问题的相互作用的属性在数据挖掘。在本文中，我们解释了如何利用SGA的计算能力，执行有效的适应广泛的适应函数。基于一个实际的健身功能可能属于这一广泛的类相对容易，我们提出了一个新的假设的工作遗传算法。我们解释了为什么我们的假设是优于积木假设，并通过实证验证的方式，我们提出了一个实验的结果，其中使用一个简单的机制，称为夹紧显着提高了性能的SGA与均匀交叉大，随机生成的MAX 3-SAT问题的实例。
英文摘要：
We recently reported that the simple genetic algorithm (SGA) is capable of performing a remarkable form of sublinear computation which has a straightforward connection with the general problem of interacting attributes in data-mining. In this paper we explain how the SGA can leverage this computational proficiency to perform efficient adaptation on a broad class of fitness functions. Based on the relative ease with which a practical fitness function might belong to this broad class, we submit a new hypothesis about the workings of genetic algorithms. We explain why our hypothesis is superior to the building block hypothesis, and, by way of empirical validation, we present the results of an experiment in which the use of a simple mechanism called clamping dramatically improved the performance of an SGA with uniform crossover on large, randomly generated instances of the MAX 3-SAT problem.
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