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Publication Detail
Convergence Rates for the Distribution of Program Outputs
  • Publication Type:
  • Authors:
    Langdon WB
  • Publisher:
    Morgan Kaufmann
  • Publication date:
  • Place of publication:
    San Francisco, US
  • Pagination:
    812, 819
  • Published proceedings:
    GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference: July 9-13, 2002
  • Editors:
    Langdon WB,Wegener J,Bull L,Potter MA,Schultz AC,Miller JF,Burke E,Jonoska N,CantĂș-Paz E,Mathias KE,Roy R,Davis D,Poli R,Balakrishnan K,Honavar VG,Rudolph G
  • ISBN-10:
  • Status:
  • Keywords:
    CONFERENCE, evolutionary, genetic, NEW-YORK
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain convergence theorems give general upper bounds on the linear program sizes needed for convergence. Tight bounds (exponential in N, N log N, and smaller) are given for five computer models (any, average, cyclic, bit flip and Boolean). Mutation randomizes a genetic algorithm population in 0.25 (l+1)(log(l)+4) generations. Results for a genetic programming (GP) like model are confirmed by experiment.
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