论文标题
多代选择在几何语义遗传编程中的影响
The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming
论文作者
论文摘要
在进化方法中，一种非常突出的方法是遗传编程，近年来，一种称为几何语义遗传编程（GSGP）的变体已证明已成功适用于许多现实世界中的问题。由于其实施的特殊性，GSGP需要存储所有进化历史，即第一个人口。我们利用这些存储的信息来定义能够使用来自较老人群的个人的多代选择方案。我们表明，使用“旧世代”的有限能力实际上对搜索过程有用，从而显示了提高GSGP性能的零成本方式。
Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all the evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use "old" generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP.
