Dynamic Parameter Encoding for Genetic Algorithms

N. N. Schraudolph and R. K. Belew. Dynamic Parameter Encoding for Genetic Algorithms. Machine Learning, 9:9–21, 1992.

Download

216.5kB	102.9kB	76.2kB

Abstract

The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models.

BibTeX Entry

@article{SchBel92,
     author = {Nicol N. Schraudolph and Richard K. Belew},
      title = {\href{http://nic.schraudolph.org/pubs/SchBel92.pdf}{
               Dynamic Parameter Encoding for Genetic Algorithms}},
      pages = {9--21},
    journal = {Machine Learning},
     volume =  9,
       year =  1992,
   b2h_type = {Journal Papers},
  b2h_topic = {Evolutionary Algorithms},
   abstract = {
    The common use of static binary place-value codes for real-valued
    parameters of the phenotype in Holland's genetic algorithm (GA) forces
    either the sacrifice of representational precision for efficiency of
    search or vice versa.  {\em Dynamic Parameter Encoding}\/ (DPE) is a
    mechanism that avoids this dilemma by using convergence statistics
    derived from the GA population to adaptively control the mapping
    from fixed-length binary genes to real values.  DPE is shown to be
    empirically effective and amenable to analysis; we explore the problem
    of {\em premature convergence}\/ in GAs through two convergence models.
}}