Document Type

Article

Publication Title

Applications of Mathematics

Publisher

Springer Berlin Heidelberg

Publication Date

10-2014

Volume

59

Issue

5

Disciplines

Computer Sciences

Abstract

Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic algorithms for optimization, which mimic operators from natural selection and genetics. The paper analyses the convergence of the heuristic associated to a special type of Genetic Algorithm, namely the Steady State Genetic Algorithm (SSGA), considered as a discrete-time dynamical system non-generational model. Inspired by the Markov chain results in finite Evolutionary Algorithms, conditions are given under which the SSGA heuristic converges to the population consisting of copies of the best chromosome.

Keywords

genetic algorithm, Markov chain, random heuristic search MSC 2010: 60J10, 68W20, 90C59

DOI

10.1007/s10492-014-0069-z

Comments

The original publication is available at www.dml.cz

Link to publisher's version: http://link.springer.com/article/10.1007%2Fs10492-014-0069-z

Rights

© Springer International Publishing AG, Part of Springer Science+Business Media

Download

Share

COinS