Dynamic Parameter Encoding for Genetic Algorithms

Authors:
Nicol N. Schraudolph;Richard K. Belew
Affiliations:
Computer Science & Engineering Department, University of California, San Diego, La Jolla, CA 92093-0114. nici@cs.ucsd.edu;Computer Science & Engineering Department, University of California, San Diego, La Jolla, CA 92093-0114. rik@cs.ucsd.edu
Venue:
Machine Learning
Year:
1992

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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.