## 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.

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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.

### 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.
}}


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