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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)

Abstract

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 (\(\mu \)+1) GA and the \(\mathrm {Jump}_k\) 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 \(\varOmega (n/\log n)\) compared to mutation-only algorithms like the (1+1) EA.

Keywords

Genetic algorithms Crossover Diversity Runtime analysis Theory 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Duc-Cuong Dang
    • 1
  • Tobias Friedrich
    • 2
  • Timo Kötzing
    • 2
  • Martin S. Krejca
    • 2
  • Per Kristian Lehre
    • 1
  • Pietro S. Oliveto
    • 3
  • Dirk Sudholt
    • 3
  • Andrew M. Sutton
    • 2
  1. 1.University of NottinghamNottinghamUK
  2. 2.Hasso Plattner InstitutePotsdamGermany
  3. 3.University of SheffieldSheffieldUK

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