1. Genetic Algorithm and Their Applications to Scheduling
Hyungjun Park

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

3. Biological Background
Genes
Natural Selection and Fitness
Reproduction
Crossover
Mutation

4. Genetic Algorithm
Start
Repeat Until
End Condition
End

5. 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

6. 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%

7. Q & A