This paper discusses two feasibility studies of
Genetic Programming (GP) to the field of control
theory, GP being a method inspired from nature where
the goal is to create a computer program automatically
from high-level statements of problems' requirements.
The first feasibility study derives from stability
theory and deals with evolving a program that can solve
discrete-time Lyapunov equations. The second
application of GP tackles the problem of producing a
self-evolved Model Reference Adaptive System (MRAS).
Basic structure of the programs used in the experiments
are only marginally different, yet applied to seemingly
quite different problems. In the first feasibility
study, it was observed that GP, beside correct usage of
global variables, could also purposely arrange
mathematical functions and operations in an iterative
manner without being explicitly programmed for the
task. In the second feasibility study, a controller was
evolved for a second-order process based on a
pre-defined reference model.
%0 Journal Article
%1 KuanLuenNg:2002:IRS
%A Ng, Kuan Luen
%A Johansson, Rolf
%D 2002
%J Journal of Intelligent and Robotic Systems
%K Lyapunov adaptive algorithms, control, functions, genetic learning model programming, reference systems systems,
%N 3
%P 289--307
%R doi:10.1023/A:1021123520925
%T Evolving Programs and Solutions Using Genetic
Programming with Application to Learning and Adaptive
Control
%V 35
%X This paper discusses two feasibility studies of
Genetic Programming (GP) to the field of control
theory, GP being a method inspired from nature where
the goal is to create a computer program automatically
from high-level statements of problems' requirements.
The first feasibility study derives from stability
theory and deals with evolving a program that can solve
discrete-time Lyapunov equations. The second
application of GP tackles the problem of producing a
self-evolved Model Reference Adaptive System (MRAS).
Basic structure of the programs used in the experiments
are only marginally different, yet applied to seemingly
quite different problems. In the first feasibility
study, it was observed that GP, beside correct usage of
global variables, could also purposely arrange
mathematical functions and operations in an iterative
manner without being explicitly programmed for the
task. In the second feasibility study, a controller was
evolved for a second-order process based on a
pre-defined reference model.
@article{KuanLuenNg:2002:IRS,
abstract = {This paper discusses two feasibility studies of
Genetic Programming (GP) to the field of control
theory, GP being a method inspired from nature where
the goal is to create a computer program automatically
from high-level statements of problems' requirements.
The first feasibility study derives from stability
theory and deals with evolving a program that can solve
discrete-time Lyapunov equations. The second
application of GP tackles the problem of producing a
self-evolved Model Reference Adaptive System (MRAS).
Basic structure of the programs used in the experiments
are only marginally different, yet applied to seemingly
quite different problems. In the first feasibility
study, it was observed that GP, beside correct usage of
global variables, could also purposely arrange
mathematical functions and operations in an iterative
manner without being explicitly programmed for the
task. In the second feasibility study, a controller was
evolved for a second-order process based on a
pre-defined reference model.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Ng, Kuan Luen and Johansson, Rolf},
biburl = {https://www.bibsonomy.org/bibtex/21e3a9caba8305a16e33e2c3a0424d982/brazovayeye},
doi = {doi:10.1023/A:1021123520925},
interhash = {2f1aff160eb5a60d9de3e865d1c6e941},
intrahash = {1e3a9caba8305a16e33e2c3a0424d982},
journal = {Journal of Intelligent and Robotic Systems},
keywords = {Lyapunov adaptive algorithms, control, functions, genetic learning model programming, reference systems systems,},
month = {November},
notes = {Article ID: 399272},
number = 3,
pages = {289--307},
timestamp = {2008-06-19T17:48:13.000+0200},
title = {Evolving Programs and Solutions Using Genetic
Programming with Application to Learning and Adaptive
Control},
volume = 35,
year = 2002
}