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Evolving Programs and Solutions Using Genetic Programming with Application to Learning and Adaptive Control

Ng, Kuan Luen and Johansson, Rolf LU (2002) In Journal of Intelligent & Robotic Systems 35(3). p.289-307
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
model reference adaptive systems, learning systems, adaptive control, genetic programming, Lyapunov functions
in
Journal of Intelligent & Robotic Systems
volume
35
issue
3
pages
289 - 307
publisher
Kluwer
external identifiers
  • wos:000179369100004
  • scopus:0036866419
ISSN
0921-0296
DOI
10.1023/A:1021123520925
language
English
LU publication?
yes
id
cd4fcb86-7b40-409e-9a5d-0ff6d27030ff (old id 162038)
date added to LUP
2007-07-09 13:16:58
date last changed
2017-01-01 07:07:37
@article{cd4fcb86-7b40-409e-9a5d-0ff6d27030ff,
  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.},
  author       = {Ng, Kuan Luen and Johansson, Rolf},
  issn         = {0921-0296},
  keyword      = {model reference adaptive systems,learning systems,adaptive control,genetic programming,Lyapunov functions},
  language     = {eng},
  number       = {3},
  pages        = {289--307},
  publisher    = {Kluwer},
  series       = {Journal of Intelligent & Robotic Systems},
  title        = {Evolving Programs and Solutions Using Genetic Programming with Application to Learning and Adaptive Control},
  url          = {http://dx.doi.org/10.1023/A:1021123520925},
  volume       = {35},
  year         = {2002},
}