Biased and unbiased random-key genetic algorithms: An experimental analysis

  • José Fernando Gonçalves
  • Mauricio G.C. Resende
  • Rodrigo F. Toso
    • Categories Meta Heuristics, Optimization Software Benchmark, Stochastic Approaches Tags , , , , , , Short URL: https://optimization-online.org/?p=12287

    We study the runtime performance of three types of random-key genetic algorithms: the unbiased algorithm of Bean (1994); the biased algorithm of Gonçalves and Resende (2011); and a greedy version of Bean's algorithm on 12 instances from four types of covering problems: general-cost set covering, Steiner triple covering, general-cost set K-covering, and unit-cost covering by pairs. The experiments show that, in 11 of the 12 instances, the greedy version of Bean's algorithm is faster than Bean's original method and that the biased variant is faster than both variants of Bean's algorithm.

    Citation

    AT&T Labs Research Technical Report, Florham Park, NJ, Dec. 2012.

    Article

    Download

    View Biased and unbiased random-key genetic algorithms: An experimental analysis