1. Genetic Algorithm(GA)
ZhiZhong Zhuang

2. Genetic Algorithm Develop from Darwin’s Theory of Evolution
A method that searching the optimal solution by simulating the processing of natural evolution

3. Darwin’s Theory of Evolution
Natural Selection
Chromosome (gene)
Crossover
Mutation

4. Apply to Algorithm
Nature select from initial species Algorithm select from Initial set Nature select better species Algorithm select better solution

5. Genetic Algorithm - Initialization
Coding – can not deal with parameters directly
Setting evolution generation counter t = 0, set the maximum evolution number T
Create an initial population which is usually generate randomly

6. Genetic Algorithm - Evaluation
Create fitness function
Directly affects the performance of Genetic algorithm.
Calculating fitness value of each individual

7. Genetic Algorithm - Selection
Select the individual who has better fitness value
Eliminate low fitness value individual
Improve fitness value

8. Genetic Algorithm - Crossover
Exchange gene of previous generation with certain probability
Get new individual
Most important part to get better individual

9. Genetic Algorithm - Mutation
Small probability event but important
Get new individual
Repeat evaluation, selection, crossover, mutation

10. Genetic Algorithm - End
The fitness value of the best individual reaches a given standard value
The optimal fitness value of individuals and groups are no longer rising
Generation counter equal to maximum evolution number T

11. Features of GA
Genetic algorithms process multiple individuals of group, evaluate multiple solution in searching space, reduce the risk to get local optimal solution
Evaluate individual by fitness function only, this can make GA apply widely
Use different fitness function to get different search direction

12. Thank you Any question