Suppr超能文献

表示不变的遗传算子。

Representation invariant genetic operators.

机构信息

School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.

出版信息

Evol Comput. 2010 Winter;18(4):635-60. doi: 10.1162/EVCO_a_00007. Epub 2010 Jun 28.

Abstract

A genetic algorithm is invariant with respect to a set of representations if it runs the same no matter which of the representations is used. We formalize this concept mathematically, showing that the representations generate a group that acts upon the search space. Invariant genetic operators are those that commute with this group action. We then consider the problem of characterizing crossover and mutation operators that have such invariance properties. In the case where the corresponding group action acts transitively on the search space, we provide a complete characterization, including high-level representation-independent algorithms implementing these operators.

摘要

如果遗传算法在使用不同表示方式时运行结果相同,则它对一组表示方式是不变的。我们通过数学形式化这个概念,证明这些表示方式生成了一个作用于搜索空间的群。不变的遗传算子是那些与这个群作用可交换的算子。然后,我们考虑了具有这种不变性的交叉和变异算子的特征化问题。在对应的群作用在搜索空间上传递的情况下,我们提供了一个完整的特征化,包括实现这些算子的高层、表示无关的算法。

相似文献

1
Representation invariant genetic operators.
Evol Comput. 2010 Winter;18(4):635-60. doi: 10.1162/EVCO_a_00007. Epub 2010 Jun 28.
2
Crossover invariant subsets of the search space for evolutionary algorithms.
Evol Comput. 2004 Spring;12(1):19-46. doi: 10.1162/evco.2004.12.1.19.
3
Group properties of crossover and mutation.
Evol Comput. 2002 Summer;10(2):151-84. doi: 10.1162/106365602320169839.
4
Structural search spaces and genetic operators.
Evol Comput. 2004 Winter;12(4):461-93. doi: 10.1162/1063656043138941.
5
Representations and evolutionary operators for the scheduling of pump operations in water distribution networks.
Evol Comput. 2011 Fall;19(3):429-67. doi: 10.1162/EVCO_a_00035. Epub 2011 Jun 20.
6
Fitness landscapes, memetic algorithms, and greedy operators for graph bipartitioning.
Evol Comput. 2000 Spring;8(1):61-91. doi: 10.1162/106365600568103.
7
Theoretical foundations of spatially-variant mathematical morphology part I: binary images.
IEEE Trans Pattern Anal Mach Intell. 2008 May;30(5):823-36. doi: 10.1109/TPAMI.2007.70754.
8
Multidimensional knapsack problem: a fitness landscape analysis.
IEEE Trans Syst Man Cybern B Cybern. 2008 Jun;38(3):604-16. doi: 10.1109/TSMCB.2008.915539.
10
RGFGA: an efficient representation and crossover for grouping genetic algorithms.
Evol Comput. 2005 Winter;13(4):477-99. doi: 10.1162/106365605774666903.

引用本文的文献

1
How can selection of biologically inspired features improve the performance of a robust object recognition model?
PLoS One. 2012;7(2):e32357. doi: 10.1371/journal.pone.0032357. Epub 2012 Feb 27.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验