Four-group equation of genetic algorithm

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A nonlinear differential equation system of only four variables is proposed to describe the time-dependent appearance probabilities of the best-adapted gene and the other three mutant groups in the genetic algorithm. All the strings are classified into four groups according to the Hamming distance. Each group has the temporal-dependent frequency distribution plotted against the fitness-value, which is called the fitness-probability landscape. The present simple model for the genetic algorithm is an analogical one such as the Lorentz model for the thermal convection flow. Several types of evolutionary routes are predicted by varying three metabolic-rate-controlling parameters and the problem. The predictions are confirmed by performing numerical calculations with a concrete genetic algorithm. Artificial constants in the genetic algorithm can be optimized by using the present theoretical approach before the trial of evolution is performed.

Original languageEnglish
Pages (from-to)240-248
Number of pages9
JournalJSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing
Volume38
Issue number2
Publication statusPublished - 1995 Jun
Externally publishedYes

Fingerprint

Genetic algorithms
Hamming distance
Differential equations
Genes
Concretes

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Four-group equation of genetic algorithm. / Naitoh, Ken.

In: JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Manufacturing, Vol. 38, No. 2, 06.1995, p. 240-248.

Research output: Contribution to journalArticle

@article{5eb1640daf7546a398a3059039a4948b,
title = "Four-group equation of genetic algorithm",
abstract = "A nonlinear differential equation system of only four variables is proposed to describe the time-dependent appearance probabilities of the best-adapted gene and the other three mutant groups in the genetic algorithm. All the strings are classified into four groups according to the Hamming distance. Each group has the temporal-dependent frequency distribution plotted against the fitness-value, which is called the fitness-probability landscape. The present simple model for the genetic algorithm is an analogical one such as the Lorentz model for the thermal convection flow. Several types of evolutionary routes are predicted by varying three metabolic-rate-controlling parameters and the problem. The predictions are confirmed by performing numerical calculations with a concrete genetic algorithm. Artificial constants in the genetic algorithm can be optimized by using the present theoretical approach before the trial of evolution is performed.",
author = "Ken Naitoh",
year = "1995",
month = "6",
language = "English",
volume = "38",
pages = "240--248",
journal = "JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Menufacturing",
issn = "1340-8062",
publisher = "Japan Society of Mechanical Engineers",
number = "2",

}

TY - JOUR

T1 - Four-group equation of genetic algorithm

AU - Naitoh, Ken

PY - 1995/6

Y1 - 1995/6

N2 - A nonlinear differential equation system of only four variables is proposed to describe the time-dependent appearance probabilities of the best-adapted gene and the other three mutant groups in the genetic algorithm. All the strings are classified into four groups according to the Hamming distance. Each group has the temporal-dependent frequency distribution plotted against the fitness-value, which is called the fitness-probability landscape. The present simple model for the genetic algorithm is an analogical one such as the Lorentz model for the thermal convection flow. Several types of evolutionary routes are predicted by varying three metabolic-rate-controlling parameters and the problem. The predictions are confirmed by performing numerical calculations with a concrete genetic algorithm. Artificial constants in the genetic algorithm can be optimized by using the present theoretical approach before the trial of evolution is performed.

AB - A nonlinear differential equation system of only four variables is proposed to describe the time-dependent appearance probabilities of the best-adapted gene and the other three mutant groups in the genetic algorithm. All the strings are classified into four groups according to the Hamming distance. Each group has the temporal-dependent frequency distribution plotted against the fitness-value, which is called the fitness-probability landscape. The present simple model for the genetic algorithm is an analogical one such as the Lorentz model for the thermal convection flow. Several types of evolutionary routes are predicted by varying three metabolic-rate-controlling parameters and the problem. The predictions are confirmed by performing numerical calculations with a concrete genetic algorithm. Artificial constants in the genetic algorithm can be optimized by using the present theoretical approach before the trial of evolution is performed.

UR - http://www.scopus.com/inward/record.url?scp=0029327068&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029327068&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0029327068

VL - 38

SP - 240

EP - 248

JO - JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Menufacturing

JF - JSME International Journal, Series C: Dynamics, Control, Robotics, Design and Menufacturing

SN - 1340-8062

IS - 2

ER -