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Zorica Stanimirovi ć Faculty of Mathematics, Belgrade zoricast @ matf.bg.ac.rs

Zorica Stanimirovi ć Faculty of Mathematics, Belgrade zoricast @ matf.bg.ac.rs. decoded individuals. Population of individuals. Evaluation Selection of best fitted individuals. Mutation. parents. offspring. Crossover. -maximal number of GA generations

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Zorica Stanimirovi ć Faculty of Mathematics, Belgrade zoricast @ matf.bg.ac.rs

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  1. ZoricaStanimirović Faculty of Mathematics, Belgrade zoricast@matf.bg.ac.rs

  2. decoded individuals Population of individuals Evaluation Selection of best fitted individuals Mutation parents offspring Crossover

  3. -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...

  4. -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

  5. -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

  6. 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

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