This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)
Intelligent Optimization via Learnable Evolution Model
Arlington, Virginia
November 13-November 15
ISBN: 0-7695-2728-0
ASCII Text
x 
 
Ryszard S. Michalski, Janusz Wojtusiak, Kenneth A. Kaufman, "Intelligent Optimization via Learnable Evolution Model," 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, pp. 332-335, 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006.
 
 
BibTex
x
 
@article{ 10.1109/ICTAI.2006.69,
author = {Ryszard S. Michalski and Janusz Wojtusiak and Kenneth A. Kaufman},
title = {Intelligent Optimization via Learnable Evolution Model},
journal ={2012 IEEE 24th International Conference on Tools with Artificial Intelligence},
volume = {0},
year = {2006},
issn = {1082-3409},
pages = {332-335},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.69},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence
TI - Intelligent Optimization via Learnable Evolution Model
SN - 1082-3409
SP332
EP335
A1 - Ryszard S. Michalski,
A1 - Janusz Wojtusiak,
A1 - Kenneth A. Kaufman,
PY - 2006
KW - null
VL - 0
JA - 2012 IEEE 24th International Conference on Tools with Artificial Intelligence
ER -
 
Ryszard S. Michalski, George Mason University
Janusz Wojtusiak, George Mason University
Kenneth A. Kaufman, George Mason University
A new method for optimizing complex functions and systems is described that employs Learnable Evolution Model (LEM), a form of non-Darwinian evolutionary computation guided by machine learning. LEM?s main novelties are operators for creating new individuals that include hypothesis generation, which learns rules indicating subareas in the search space likely containing the optimum, and hypothesis instantiation, which populates these subareas with new candidate solutions. LEM3, the newest and most advanced implementation of learnable evolution, is briefly described and experimentally compared with other evolutionary computation programs on selected function optimization problems. We also describe two specialized LEM-based systems for heat exchanger optimization.
Citation:
Ryszard S. Michalski, Janusz Wojtusiak, Kenneth A. Kaufman, "Intelligent Optimization via Learnable Evolution Model," ictai, pp.332-335, 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006
Usage of this product signifies your acceptance of the Terms of Use.