In this paper a genetic algorithm is proposed where
the worst individual and individuals with indices close
to its index are replaced in every generation by
randomly generated individuals for dynamic optimisation
problems. In the proposed genetic algorithm, the
replacement of an individual can affect other
individuals in a chain reaction. The new individuals
are preserved in a subpopulation which is defined by
the number of individuals created in the current chain
reaction. If the values of fitness are similar, as is
the case with small diversity, one single replacement
can affect a large number of individuals in the
population. This simple approach can take the system to
a self-organising behaviour, which can be useful to
control the diversity level of the population and hence
allows the genetic algorithm to escape from local
optima once the problem changes due to the dynamics.
%0 Journal Article
%1 Tinos:2007:GPEM
%A Tinos, Renato
%A Yang, Shengxiang
%D 2007
%J Genetic Programming and Evolvable Machines
%K Dynamic Random Self-organised algorithms, criticality, genetic immigrants optimisation problems,
%N 3
%P 255--286
%R doi:10.1007/s10710-007-9024-z
%T A self-organizing random immigrants genetic algorithm
for dynamic optimization problems
%V 8
%X In this paper a genetic algorithm is proposed where
the worst individual and individuals with indices close
to its index are replaced in every generation by
randomly generated individuals for dynamic optimisation
problems. In the proposed genetic algorithm, the
replacement of an individual can affect other
individuals in a chain reaction. The new individuals
are preserved in a subpopulation which is defined by
the number of individuals created in the current chain
reaction. If the values of fitness are similar, as is
the case with small diversity, one single replacement
can affect a large number of individuals in the
population. This simple approach can take the system to
a self-organising behaviour, which can be useful to
control the diversity level of the population and hence
allows the genetic algorithm to escape from local
optima once the problem changes due to the dynamics.
@article{Tinos:2007:GPEM,
abstract = {In this paper a genetic algorithm is proposed where
the worst individual and individuals with indices close
to its index are replaced in every generation by
randomly generated individuals for dynamic optimisation
problems. In the proposed genetic algorithm, the
replacement of an individual can affect other
individuals in a chain reaction. The new individuals
are preserved in a subpopulation which is defined by
the number of individuals created in the current chain
reaction. If the values of fitness are similar, as is
the case with small diversity, one single replacement
can affect a large number of individuals in the
population. This simple approach can take the system to
a self-organising behaviour, which can be useful to
control the diversity level of the population and hence
allows the genetic algorithm to escape from local
optima once the problem changes due to the dynamics.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Tinos, Renato and Yang, Shengxiang},
biburl = {https://www.bibsonomy.org/bibtex/2860520e90f2ff9e451bc4ef4836daae6/brazovayeye},
doi = {doi:10.1007/s10710-007-9024-z},
interhash = {dfda1f45958855d4124053b7ff68ac74},
intrahash = {860520e90f2ff9e451bc4ef4836daae6},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {Dynamic Random Self-organised algorithms, criticality, genetic immigrants optimisation problems,},
month = {Septembe},
number = 3,
pages = {255--286},
size = {32 pages},
timestamp = {2008-06-19T17:53:09.000+0200},
title = {A self-organizing random immigrants genetic algorithm
for dynamic optimization problems},
volume = 8,
year = 2007
}