Implicitly Controlling Bloat in Genetic Programming
Implicitly Controlling Bloat in Genetic Programming
复制标题
隐式控制遗传编程中的膨胀
DOI：
10.1109/tevc.2009.2027314
复制
发表时间：
2010
影响因子：
14.3
通讯作者：
Grant Dick
中科院分区：
文献类型：
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作者：
P. Whigham;Grant Dick
在使用遗传编程 (GP) 解决方案的演化过程中，平均树大小通常会增加，而适应度却没有相应增加，这种现象通常称为膨胀。尽管之前从理论和实践的角度进行了研究，但在导出不明确涉及树大小的膨胀控制方面进展甚微。在这里，空间人口结构与局部精英替代的结合使用被证明可以减少膨胀，而不会导致性能损失。有关近亲繁殖和精英主义作用的理论概念用于支持所描述的方法。通过对基准问题进行广泛的计算机模拟来确认所提出的系统行为。主要的实际结果是，通过将群体放置在环面上，并通过摩尔邻域和局部精英替换定义选择，可以在不影响性能的情况下大幅减少膨胀。
During the evolution of solutions using genetic programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness-a phenomenon commonly referred to as bloat. Although previously studied from theoretical and practical viewpoints there has been little progress in deriving controls for bloat which do not explicitly refer to tree size. Here, the use of spatial population structure in combination with local elitist replacement is shown to reduce bloat without a subsequent loss of performance. Theoretical concepts regarding inbreeding and the role of elitism are used to support the described approach. The proposed system behavior is confirmed via extensive computer simulations on benchmark problems. The main practical result is that by placing a population on a torus, with selection defined by a Moore neighborhood and local elitist replacement, bloat can be substantially reduced without compromising performance.
DOI：
--
发表时间：
1992
期刊：
--
影响因子：
--
作者：
J. Koza
通讯作者：
J. Koza