渡线
遗传程序设计
树(集合论)
突变
操作员(生物学)
遗传算法
财产(哲学)
计算机科学
质量(理念)
数学
数学优化
人工智能
组合数学
生物
遗传学
物理
哲学
量子力学
抑制因子
认识论
基因
转录因子
作者
Terry Van Belle,David H. Ackley
标识
DOI:10.1007/3-540-45984-7_15
摘要
The traditional genetic programming crossover and mutation operators have the property that they tend to affect smaller and smaller fractions of a solution tree as the tree grows larger. It is generally thought that this property contributes to the ‘code bloat’ problem, in which evolving solution trees rapidly become unmanageably large, and researchers have investigated alternate operators designed to avoid this effect. We introduce one such operator, called uniform subtree mutation (USM), and investigate its performance—alone and in combination with traditional crossover-on six standard problems. We measure its behavior using both computational effort and size effort, a variation that takes tree size into account. Our tests show that genetic programming using pure USM reduces evolved tree sizes dramatically, compared to crossover, but does impact solution quality somewhat. In some cases, however, a combination of USM and crossover yielded both smaller trees and superior performance, as measured both by size effort and traditional metrics.
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