The Max problem revisited: The importance of mutation in genetic programming

Timo Kötzing, Andrew M. Sutton, Frank Neumann, Una May O'Reilly

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We study the importance of mutation in genetic programming and contribute to the rigorous understanding of genetic programming algorithms by providing runtime complexity analyses for the well-known Max problem. Several experimental studies have indicated that it is hard to solve the Max problem with crossover-based algorithms. Our analyses show that different variants of the Max problem can provably be solved efficiently 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.

Original languageEnglish (US)
Pages (from-to)94-107
Number of pages14
JournalTheoretical Computer Science
Volume545
Issue numberC
DOIs
StatePublished - 2014

Keywords

  • Genetic programming
  • Mutation
  • Runtime analysis
  • Theory

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