TY - GEN
T1 - Population based incremental learning with guided mutation versus genetic algorithms
T2 - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
AU - Gosling, Timothy
AU - Jin, Nanlin
AU - Tsang, Edward
N1 - Funding Information:
This study was supported by the grant from Samuel Waxman Cancer Research Foundation Tumor Dormancy Program, NIH/National Cancer Institute (CA109182, CA163131), NIEHS (ES017146) and NYSTEM to JAA-G.
PY - 2005
Y1 - 2005
N2 - Axelrod's original experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that GA performs slightly better than standard PBIL under most conditions. This differnce in performance can be mitigated and reversed through the use of a 'guided' mutation operator.
AB - Axelrod's original experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that GA performs slightly better than standard PBIL under most conditions. This differnce in performance can be mitigated and reversed through the use of a 'guided' mutation operator.
UR - http://www.scopus.com/inward/record.url?scp=27144467053&partnerID=8YFLogxK
M3 - Conference Proceeding
AN - SCOPUS:27144467053
SN - 0780393635
T3 - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
SP - 958
EP - 965
BT - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Y2 - 2 September 2005 through 5 September 2005
ER -