A network theoretic analysis of evolutionary algorithms

Karthik Kuber, Stuart W. Card, Kishan G. Mehrotra, Chilukuri K. Mohan

Research output: Chapter in Book/Entry/PoemConference contribution

2 Scopus citations


Network theoretic analyses have been shown to be extremely useful in multiple fields and applications. We propose this approach to study the dynamic behavior of evolutionary algorithms, the first such analysis to the best of our knowledge. Evolving populations are represented as dynamic networks, and we show that changes in population characteristics can be recognized at the level of the networks representing successive generations, with implications for possible improvements in the evolutionary algorithm, e.g., in deciding when a population is prematurely converging, and when a reinitialization of the population may be beneficial to reduce computational effort. In this paper, we show that network-theoretic analyses of evolutionary algorithms help in: (i) studying community-level behaviors, and (ii) using graph properties and metrics to analyze evolutionary algorithms.

Original languageEnglish (US)
Title of host publicationSwarm, Evolutionary, and Memetic Computing - Third International Conference, SEMCCO 2012, Proceedings
Number of pages9
StatePublished - 2012
Event3rd International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2012 - Bhubaneswar, India
Duration: Dec 20 2012Dec 22 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7677 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2012


  • Dynamic Networks
  • Early Detection of Convergence
  • Evolutionary Algorithms
  • Genetic Algorithms

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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