Emergence of diversity and its benefits for crossover in genetic algorithms

Duc Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


Population diversity is essential for avoiding premature convergence in Genetic Algorithms (GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges in populations are not well understood.We use rigorous runtime analysis to gain insight into population dynamics and GA performance for a standard (μ+1) GA and the Jumpk test function. By studying the stochastic process underlying the size of the largest collection of identical genotypes we show that the interplay of crossover followed by mutation may serve as a catalyst leading to a sudden burst of diversity. This leads to improvements of the expected optimisation time of order Ω(n/ log n) compared to mutationonly algorithms like the (1+1) EA.

Original languageEnglish (US)
Title of host publicationParallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings
EditorsEmma Hart, Ben Paechter, Julia Handl, Manuel López-Ibáñez, Peter R. Lewis, Gabriela Ochoa
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319458229
StatePublished - 2016
Externally publishedYes
Event14th International Conference on Parallel Problem Solving from Nature, PPSN 2016 - Edinburgh, United Kingdom
Duration: Sep 17 2016Sep 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9921 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Parallel Problem Solving from Nature, PPSN 2016
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 618091 (SAGE) and from the EPSRC under grant no. EP/M004252/1 and is based upon work from COST Action CA15140 ‘Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)’.

Publisher Copyright:
© Springer International Publishing AG 2016.


  • Crossover
  • Diversity
  • Genetic algorithms
  • Runtime analysis
  • Theory


Dive into the research topics of 'Emergence of diversity and its benefits for crossover in genetic algorithms'. Together they form a unique fingerprint.

Cite this