DocumentCode
2669032
Title
An experimental study of benchmarking functions for genetic algorithms
Author
Digalakis, Jason G. ; Margaritis, Konstantinos G.
Author_Institution
Dept. of Appl. Inf., Macedonia Univ., Thessaloniki, Greece
Volume
5
fYear
2000
fDate
2000
Firstpage
3810
Abstract
This paper presents a review and experimental results of major benchmarking functions used for the performance control of genetic algorithms (GAs). Parameters considered include the effect of population size, crossover probability and pseudo-random number generators. The general computational behavior of two basic GAs models, the Generational Replacement Model and the Steady State Replacement Model is evaluated
Keywords
algorithm theory; genetic algorithms; Generational Replacement Model; ISAAC; Steady State Replacement Model; benchmarking functions; crossover probability; genetic algorithms; performance control; population size; pseudo-random number generators; Algorithm design and analysis; Benchmark testing; Biological system modeling; Biological systems; Costs; Genetic algorithms; Informatics; Performance analysis; Steady-state; Telephony;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 2000 IEEE International Conference on
Conference_Location
Nashville, TN
ISSN
1062-922X
Print_ISBN
0-7803-6583-6
Type
conf
DOI
10.1109/ICSMC.2000.886604
Filename
886604
Link To Document