Using Reliability Analysis to Estimate the Number of Generations to Convergence in Genetic Algorithms

Research output: Contribution to journal › Article › peer-review

Abstract

We present a stochastic reliability model for the haploid, binary, fixed-length genetic algorithm. Empirical evidence is provided.

Original language	American English
Journal	Information Processing Letters
Volume	46
DOIs	https://doi.org/10.1016/0020-0190(93)90027-7
State	Published - Jun 25 1993
Externally published	Yes

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title = "Using Reliability Analysis to Estimate the Number of Generations to Convergence in Genetic Algorithms",

abstract = " We present a stochastic reliability model for the haploid, binary, fixed-length genetic algorithm. Empirical evidence is provided.",

author = "Chakraborty, {Uday Kumar} and Dastidar, {D. Ghosh}",

note = "We present a stochastic reliability model for the haploid, binary, fixed-length genetic algorithm. Empirical evidence is provided.",

year = "1993",

month = jun,

day = "25",

doi = "10.1016/0020-0190(93)90027-7",

language = "American English",

volume = "46",

journal = "Information Processing Letters",

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AU - Chakraborty, Uday Kumar

AU - Dastidar, D. Ghosh

N1 - We present a stochastic reliability model for the haploid, binary, fixed-length genetic algorithm. Empirical evidence is provided.

PY - 1993/6/25

Y1 - 1993/6/25

N2 - We present a stochastic reliability model for the haploid, binary, fixed-length genetic algorithm. Empirical evidence is provided.

AB - We present a stochastic reliability model for the haploid, binary, fixed-length genetic algorithm. Empirical evidence is provided.

UR - https://www.sciencedirect.com/science/article/pii/0020019093900277

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DO - 10.1016/0020-0190(93)90027-7

M3 - Article

VL - 46

JO - Information Processing Letters

JF - Information Processing Letters

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