Macroscopic kinetic equation for a genetic algorithm

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

A macroscopic kinetic equation of only four variables for a simple genetic algorithm (SGA) with an on-off type of replication operator and a crossover operator is developed and used to predict several types of evolutionary routes for a wide range of metabolic-rate-controlling parameters, initial conditions, string lengths, population sizes, and environments. The four variables correspond to the probabilities of the best-adapted species and three mutant groups into which degenerate and redundant strings are classified according to the Hamming distance (HD). The time-dependent frequency distribution along the fitness value is given by an implicit formulation. The environment is also defined in the HD-fitness value space as the frequency distribution of all the possible types of strings without redundancy. It is found that the SGA possesses the capability for exploring quasi-macroevolution.

Original languageEnglish
Pages (from-to)87-133
Number of pages47
JournalJapan Journal of Industrial and Applied Mathematics
Volume15
Issue number1
Publication statusPublished - 1998 Feb
Externally publishedYes

Fingerprint

Hamming distance
Kinetic Equation
Strings
Genetic algorithms
Hamming Distance
Genetic Algorithm
Fitness
Kinetics
Redundancy
Crossover Operator
Population Size
Mutant
Replication
Initial conditions
Predict
Formulation
Operator
Range of data

Keywords

  • Degeneracy structure
  • Evolutionary stability
  • Genetic algorithm
  • Macroscopic kinetic equation
  • Quasi-macroevolution

ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

Cite this

Macroscopic kinetic equation for a genetic algorithm. / Naitoh, Ken.

In: Japan Journal of Industrial and Applied Mathematics, Vol. 15, No. 1, 02.1998, p. 87-133.

Research output: Contribution to journalArticle

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