.png){kind=icon}
返回
Fitness uniform optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TEVC.2005.863127.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of fitter individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other hand. Motivated by a universal similarity relation on the individuals, we propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure toward sparsely populated fitness regions, not necessarily toward higher fitness, as is the case for all other selection schemes. We show analytically on a simple example that the new selection scheme can be much more effective than standard selection schemes. We also propose a new deletion scheme which achieves a similar result via deletion and show how such a scheme preserves genetic diversity more effectively than standard approaches. We compare the performance of the new schemes to tournament selection and random deletion on an artificial deceptive problem and a range of NP hard problems: traveling salesman, set covering, and satisfiability.
Keyword:
fitness tree model
fitness uniform deletion scheme
fitness uniform selection scheme
local optima
preserve diversity
satisfiability
set covering
traveling salesman
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
暂无机构信息
引用论文
Investigation of morphologies and characterization of rare earth metal samarium hexacyanoferrate and its composite with surfactant intercalated graphene oxide for sensor applications.png)
RSC Adv.
RSC Adv.IF0
PREAI
分享收藏
分享收藏分享收藏分享收藏
查看更多内容