The Max Problem Revisited: The Importance of Mutation in Genetic Programming

被引：8

作者：

Koetzing, Timo ^{[1
]}

Sutton, Andrew M. ^{[1
]}

Neumann, Frank ^{[1
]}

O'Reilly, Una-May ^{[1
]}

机构：

[1] Max Planck Inst Informat, D-66123 Saarbrucken, Germany

来源：

PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2012年

关键词：

Genetic Programming; Mutation; Theory; Runtime Analysis;

D O I：

10.1145/2330163.2330348

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper contributes to the rigorous understanding of genetic programming algorithms by providing runtime complexity analyses of the well-studied Max problem. Several experimental studies have indicated that it is hard to solve the M a x problem with crossover-based algorithms. Our analyses show that different variants of the M a x problem can provably be solved using simple mutation-based genetic programming algorithms. Our results advance the body of computational complexity analyses of genetic programming, indicate the importance of mutation in genetic programming, and reveal new insights into the behavior of mutation-based genetic programming algorithms.

引用

页码：1333 / 1340

页数：8

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