Genetic Algorithm and Their Applications to Scheduling Hyungjun Park

Introduction Based on the ideas of evolution and natural selection Pioneered by John Holland in 1960’s

Biological Background Genes Natural Selection and Fitness Reproduction Crossover Mutation

Genetic Algorithm Start Repeat Until End Condition End

Traveling Salesman Problem [Start] Random routes + heuristic [Fitness] Inverse of distance [Selection] Roulette wheel selection [Crossover] Leave blanks then refill [Mutation] Interchange Parameter Value Population Size 1,000 Mutation 3% Elitism Yes End Condition 200 Iterations

GA Performance Weighted Tardiness Descent Methods AU DES, DESO GA outperforms # of jobs DES DESO GA 50 20.78% 21.02% 33.68% 100 19.70% 19.47% 33.71% 200 17.46% 17.70% 33.72% 500 14.89% 14.81% 31.76%

Q & A