Gene expression programming (GEP) is a
genotype/phenotype system that evolves computer
programs of different sizes and shapes encoded in
linear chromosomes of fixed length. However, the
performance of basic GEP is highly dependent on the
genetic operators' rate. In this work, we present a new
algorithm called GEPSA that combines GEP and simulated
annealing (SA), and GEPSA decreases the dependence on
genetic operators' rate without impairing the
performance of GEP. Three function finding problems,
including a benchmark problem of prediction sunspots,
are tested on GEPSA, results shows that importing
simulated annealing can improve the performance of
GEP.
%0 Conference Paper
%1 Siwei:2005:pICWCNMC
%A Siwei, Jiang
%A Zhihua, Cai
%A Dan, Zeng
%A Yadong, Liu
%A Qu, Li
%B Proceedings International Conference on Wireless
Communications, Networking and Mobile Computing
%D 2005
%I IEEE
%K Expression Gene Programming, algorithms, annealing chromosomes, computer genetic genotype linear phenotype programming, programs, simulated system,
%P 1264--1267
%R doi:10.1109/WCNM.2005.1544273
%T Gene expression programming based on simulated
annealing
%V 2
%X Gene expression programming (GEP) is a
genotype/phenotype system that evolves computer
programs of different sizes and shapes encoded in
linear chromosomes of fixed length. However, the
performance of basic GEP is highly dependent on the
genetic operators' rate. In this work, we present a new
algorithm called GEPSA that combines GEP and simulated
annealing (SA), and GEPSA decreases the dependence on
genetic operators' rate without impairing the
performance of GEP. Three function finding problems,
including a benchmark problem of prediction sunspots,
are tested on GEPSA, results shows that importing
simulated annealing can improve the performance of
GEP.
@inproceedings{Siwei:2005:pICWCNMC,
abstract = {Gene expression programming (GEP) is a
genotype/phenotype system that evolves computer
programs of different sizes and shapes encoded in
linear chromosomes of fixed length. However, the
performance of basic GEP is highly dependent on the
genetic operators' rate. In this work, we present a new
algorithm called GEPSA that combines GEP and simulated
annealing (SA), and GEPSA decreases the dependence on
genetic operators' rate without impairing the
performance of GEP. Three function finding problems,
including a benchmark problem of prediction sunspots,
are tested on GEPSA, results shows that importing
simulated annealing can improve the performance of
GEP.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Siwei, Jiang and Zhihua, Cai and Dan, Zeng and Yadong, Liu and Qu, Li},
biburl = {https://www.bibsonomy.org/bibtex/23bec2bfd31474b3ba588f7f48af41d41/brazovayeye},
booktitle = {Proceedings International Conference on Wireless
Communications, Networking and Mobile Computing},
doi = {doi:10.1109/WCNM.2005.1544273},
interhash = {5315f954c03c0bc0794c45cf482051a9},
intrahash = {3bec2bfd31474b3ba588f7f48af41d41},
keywords = {Expression Gene Programming, algorithms, annealing chromosomes, computer genetic genotype linear phenotype programming, programs, simulated system,},
month = {23-26 September},
notes = {Coll. of Comput., China Univ. of Geosciences, Wuhan,
China INSPEC Accession Number:8775628},
pages = {1264--1267},
publisher = {IEEE},
timestamp = {2008-06-19T17:51:47.000+0200},
title = {Gene expression programming based on simulated
annealing},
volume = 2,
year = 2005
}