Constrained minimization utilizing GA based pattern recognition of immune system

Jongsoo Lee, Hyuk Park

Research output: Contribution to journalArticle

Abstract

The immune system has pattern recognition capabilities based on reinforced learning, memory and affinity maturation interacting between antigens and antibodies. The paper deals with the adaptation of artificial immune system into genetic algorithm based design optimization. The present study utilizes the pattern recognition from the immune system and the evolution from genetic algorithm. The basic idea is derived from the fact that designs should be distinguished whether they are usable/feasible or infeasible and should be improved towards the optimal solution. For the expression of design solutions, binary coded strings are used to represent antigens and antibodies in artificial immune system and chromosomes in genetic algorithm. The paper discusses the procedure of constrained optimization that does not rely on any detailed mathematical formulation for constraint handling. A number of mathematical function minimization problems are examined for verification, and practical engineering optimization problems including inequality constraints are explored to support the proposed strategy.

Original languageEnglish
Pages (from-to)779-788
Number of pages10
JournalJournal of Mechanical Science and Technology
Volume21
Issue number5
DOIs
Publication statusPublished - 2007 May 1

Fingerprint

Immune system
Pattern recognition
Genetic algorithms
Antigens
Antibodies
Constrained optimization
Chromosomes
Data storage equipment

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering

Cite this

@article{4e3d9ec05ccd40b995ba2f78cf53054a,
title = "Constrained minimization utilizing GA based pattern recognition of immune system",
abstract = "The immune system has pattern recognition capabilities based on reinforced learning, memory and affinity maturation interacting between antigens and antibodies. The paper deals with the adaptation of artificial immune system into genetic algorithm based design optimization. The present study utilizes the pattern recognition from the immune system and the evolution from genetic algorithm. The basic idea is derived from the fact that designs should be distinguished whether they are usable/feasible or infeasible and should be improved towards the optimal solution. For the expression of design solutions, binary coded strings are used to represent antigens and antibodies in artificial immune system and chromosomes in genetic algorithm. The paper discusses the procedure of constrained optimization that does not rely on any detailed mathematical formulation for constraint handling. A number of mathematical function minimization problems are examined for verification, and practical engineering optimization problems including inequality constraints are explored to support the proposed strategy.",
author = "Jongsoo Lee and Hyuk Park",
year = "2007",
month = "5",
day = "1",
doi = "10.1007/BF02916356",
language = "English",
volume = "21",
pages = "779--788",
journal = "Journal of Mechanical Science and Technology",
issn = "1738-494X",
publisher = "Korean Society of Mechanical Engineers",
number = "5",

}

Constrained minimization utilizing GA based pattern recognition of immune system. / Lee, Jongsoo; Park, Hyuk.

In: Journal of Mechanical Science and Technology, Vol. 21, No. 5, 01.05.2007, p. 779-788.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Constrained minimization utilizing GA based pattern recognition of immune system

AU - Lee, Jongsoo

AU - Park, Hyuk

PY - 2007/5/1

Y1 - 2007/5/1

N2 - The immune system has pattern recognition capabilities based on reinforced learning, memory and affinity maturation interacting between antigens and antibodies. The paper deals with the adaptation of artificial immune system into genetic algorithm based design optimization. The present study utilizes the pattern recognition from the immune system and the evolution from genetic algorithm. The basic idea is derived from the fact that designs should be distinguished whether they are usable/feasible or infeasible and should be improved towards the optimal solution. For the expression of design solutions, binary coded strings are used to represent antigens and antibodies in artificial immune system and chromosomes in genetic algorithm. The paper discusses the procedure of constrained optimization that does not rely on any detailed mathematical formulation for constraint handling. A number of mathematical function minimization problems are examined for verification, and practical engineering optimization problems including inequality constraints are explored to support the proposed strategy.

AB - The immune system has pattern recognition capabilities based on reinforced learning, memory and affinity maturation interacting between antigens and antibodies. The paper deals with the adaptation of artificial immune system into genetic algorithm based design optimization. The present study utilizes the pattern recognition from the immune system and the evolution from genetic algorithm. The basic idea is derived from the fact that designs should be distinguished whether they are usable/feasible or infeasible and should be improved towards the optimal solution. For the expression of design solutions, binary coded strings are used to represent antigens and antibodies in artificial immune system and chromosomes in genetic algorithm. The paper discusses the procedure of constrained optimization that does not rely on any detailed mathematical formulation for constraint handling. A number of mathematical function minimization problems are examined for verification, and practical engineering optimization problems including inequality constraints are explored to support the proposed strategy.

UR - http://www.scopus.com/inward/record.url?scp=34548150209&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548150209&partnerID=8YFLogxK

U2 - 10.1007/BF02916356

DO - 10.1007/BF02916356

M3 - Article

VL - 21

SP - 779

EP - 788

JO - Journal of Mechanical Science and Technology

JF - Journal of Mechanical Science and Technology

SN - 1738-494X

IS - 5

ER -