%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 }