Genetic algorithms: A search technique applied to behavior analysis

Mary K. Dobransky, Mark J. Wierman

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Genetic algorithms are powerful generalized search techniques. This paper shows that genetic algorithms can solve a difficult class of problems in general systems theory quickly and efficiently. Genetic algorithms appear to be ideally suited to solving the combinatorially complex problem of behavior analysis. The search space of behavior analysis experiences exponential growth as a function of the number of variables. The genetic algorithm converges after considering a small percentage of these potential solutions. The number of solutions that need to be examined by the genetic algorithm seems to be a polynomial function of the number of variables and, in fact, the growth appears to be linear.

Original languageEnglish
Pages (from-to)125-135
Number of pages11
JournalInternational Journal of General Systems
Volume24
Issue number1-2
StatePublished - 1996

Fingerprint

Genetic algorithms
Genetic Algorithm
Number of Solutions
System theory
Exponential Growth
Polynomial function
Systems Theory
Search Space
Percentage
Polynomials
Converge

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Genetic algorithms : A search technique applied to behavior analysis. / Dobransky, Mary K.; Wierman, Mark J.

In: International Journal of General Systems, Vol. 24, No. 1-2, 1996, p. 125-135.

Research output: Contribution to journalArticle

Dobransky, Mary K. ; Wierman, Mark J. / Genetic algorithms : A search technique applied to behavior analysis. In: International Journal of General Systems. 1996 ; Vol. 24, No. 1-2. pp. 125-135.
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