TY - GEN
T1 - A step size based self-adaptive mutation operator for Evolutionary Programming
AU - Hong, Libin
AU - Drake, John
AU - Özcan, Ender
PY - 2014
Y1 - 2014
N2 - The mutation operator is the only genetic operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Lévy distributions as mutation operators. According to the No Free Lunch theorem [9], no single mutation operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive mutation operator for Evolutionary Programming (SSEP). In SSEP, the mutation operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate mutation operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static mutation operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple mutation operators.
AB - The mutation operator is the only genetic operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Lévy distributions as mutation operators. According to the No Free Lunch theorem [9], no single mutation operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive mutation operator for Evolutionary Programming (SSEP). In SSEP, the mutation operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate mutation operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static mutation operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple mutation operators.
KW - Convergence
KW - Evolutionary Programming
KW - Function optimization
KW - Mutation operator
KW - Self-adaptive
KW - Step size
UR - http://www.scopus.com/inward/record.url?scp=84905666042&partnerID=8YFLogxK
U2 - 10.1145/2598394.2609873
DO - 10.1145/2598394.2609873
M3 - Conference contribution
AN - SCOPUS:84905666042
SN - 9781450328814
T3 - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
SP - 1381
EP - 1387
BT - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery
T2 - 16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion
Y2 - 12 July 2014 through 16 July 2014
ER -