Zorica Stanimirović
Faculty of Mathematics, Belgrade
[email protected]
Population
of
individuals
Evaluation
Selection of
best fitted
individuals
Mutation
offspring
decoded
individuals
Crossover
parents
-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.html
http://www.rennard.org/alife/english/gavintrgb.html
http://www.geneticprogramming.com/
http://lancet.mit.edu
http://www.genetic-programming.org/
http://www.aic.nrl.navy.mil/galist/src/ #C