MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions

Andrei Lissovoi, Carsten Witt

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We study the behavior of a population-based EA and the Max–Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find specific local optima within each of a series of phases. In particular, we prove that a (2+1) EA with genotype diversity is able to find the global optimum of the Maze function, previously considered by Kötzing and Molter [9], in polynomial time. This is then generalized to a hierarchy result stating that for every μ , a ( μ +1) EA with genotype diversity is able to track a Maze function extended over a finite alphabet of μ symbols, whereas population size μ−1 is not sufficient. Furthermore, we show that MMAS does not require additional modifications to track the optimum of the finite-alphabet Maze functions, and, using a novel drift statement to simplify the analysis, reduce the required phase length of the Maze function.
Original languageEnglish
Issue number3
Pages (from-to)554-576
Publication statusPublished - 2015


  • Ant colony optimization
  • Dynamic problems
  • Evolutionary algorithms
  • Populations
  • Runtime analysis
  • Polynomial approximation
  • Population statistics
  • Dynamic fitness function
  • Dynamic problem
  • Finite alphabet
  • Fitness functions
  • Polynomial-time
  • Population sizes
  • Run-time analysis


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