Genetic Algorithm and Their Applications to Scheduling

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Genetic Algorithm and Their Applications to
Scheduling

• Hyungjun Park

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Introduction

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

3
Biological Background

• Genes
• Natural Selection and Fitness
• Reproduction
• Crossover
• Mutation

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Genetic Algorithm
Start
Repeat Until End Condition
End
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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
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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
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Q A