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. }}