Fast re-optimization via structural diversity

  title={Fast re-optimization via structural diversity},
  author={Benjamin Doerr and Carola Doerr and Frank Neumann},
  journal={Proceedings of the Genetic and Evolutionary Computation Conference},
When a problem instance is perturbed by a small modification, one would hope to find a good solution for the new instance by building on a known good solution for the previous one. Via a rigorous mathematical analysis, we show that evolutionary algorithms, despite usually being robust problem solvers, can have unexpected difficulties to solve such re-optimization problems. When started with a random Hamming neighbor of the optimum, the (1+1) evolutionary algorithm takes Ω(n2) time to optimize… 
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A gentle introduction to theory (for non-theoreticians)
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial
A gentle introduction to theory (for non-theoreticians)
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial
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Analyzing Evolutionary Algorithms
  • T. Jansen
  • Computer Science
    Natural Computing Series
  • 2013
The author provides an introduction to the methods used to analyze evolutionary algorithms and other randomized search heuristics with a complexity-theoretical perspective, derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms.