Intelligent Optimization via Learnable Evolution Model

Author

Michalski, Ryszard S. ; Wojtusiak, Janusz ; Kaufman, Kenneth A.

Author_Institution

Machine Learning & Inference Lab., George Mason Univ., Washington, DC

fYear

2006

fDate

Nov. 2006

Firstpage

332

Lastpage

335

Abstract

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

Keywords

evolutionary computation; learning (artificial intelligence); search problems; function optimization problems; heat exchanger optimization; hypothesis generation; hypothesis instantiation; intelligent optimization; learnable evolution model; machine learning; nonDarwinian evolutionary computation; Evolutionary computation; Genetic mutations; Intelligent agent; Laboratories; Learning systems; Machine learning; Machine learning algorithms; Optimization methods; Probes; Technological innovation;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 2006. ICTAI '06. 18th IEEE International Conference on

Conference_Location

Arlington, VA

ISSN

1082-3409

Print_ISBN

0-7695-2728-0

Type

conf

DOI

10.1109/ICTAI.2006.69

Filename

4031916