Zorica Stanimirović

Faculty of Mathematics, Belgrade

zoricast@matf.bg.ac.rs

offspring

decodedindividuals

Evaluation Selection of best fitted individuals

Crossover

parents

Population of

individuals

Mutation

-maximal number of GA generations

-high similarity of individuals in the population

-the best individual is repeated maximal times

-GA has reached global optimum or the best GA solution is good enough (according to some criterion)

-limited time of the GA run….

The combination of few stopping criterions gives the best results in practice...

-generation GA: all individuals from the population are replaced in each GA generation

-stationary GA: only one part of the population is replaced

-elitistic GA: elite individuals are directly passing in the next genaration, while the remaining individuals are replaced

-GA implementation has numerous paremeters: selection, crossover, mutation rates, population size, ….

-there is no unique combination of GA parameters that guarantees sucessful GA implementation for all problems

-the parameter values may fixed in advance or they can change during the GA run

-fixed parameter change

-adaptive parameter change

http://www.ai-junkie.com/ga/intro/gat1.htmlhttp://www.rennard.org/alife/english/gavintrgb.htmlhttp://www.geneticprogramming.com/http://lancet.mit.eduhttp://www.genetic-programming.org/http://www.aic.nrl.navy.mil/galist/src/ #C