A short slide show that give u a general view of TSP

1. Artificial Intelligence Genetic Algorithms and Travel Salesman Problem Giáo viên hướng dẫn: Trần Cao Trưởng Sinh viên thực hiện: Nguyễn Đức Hiển Lê Hữu Sơn Tùng

2. Artificial Intelligence PowerPoint has new layouts that give you more ways to present your words, images and media. Fun fact about salesmen today Genetic Algorithms(GAs) and Travel Salesman Problem(TSP)

3. 1. TSP – Overview(p.1) Hello eveyone ! I’m a HANDSOME salesman.

4. 1. TSP – Overview(p.2) As a salesman, I have to travel through many cities for business. But I just need to travel EACH city ONCE. City 1 City 3 City 2 City n-1 City n-2 MAP City n

5. 1. TSP – Overview(p.3)  I have some problems. I don’t have much time and money. 

6. 1. TSP – Overview(p.4)  Can you help me to find the way with minimum cost ?

7. 1. TSP – Traditional Solutions(p.1) BRUTE- FORCE

8. 2.GAs – Overview (p.1) GAs simulate the evolution of one population (for example human population) : based on assessing and selecting the fittest solutions in the problem population. So GAs has some similar components as an evolution in the real world: + POPULATION + INDIVIDUALS + CHROMOSOMES + OFFSPRING + CROSSOVER + MUTATION + FITNESS

9. 2.GAs – Overview (p.2) GAs

10. 2.GAs – Why use GAs to solve TSP ? (p.1) Reasons to use GAs to solve TSP includes the followings: Because the map of cities in TSP is a complete graph so searching space in TSP is very huge. It’s possbile for GAs to find out an acceptable solution due to given conditions and constraints(ex : time constraint, etc.).

11. 3.Using GAs to solve TSP? (p.1) A generation in TSP includes the following components:

12. 3.Using GAs to solve TSP? (p.2) GA Structure: GA( ) { t=0;// epoch Initialize(t); Evaluate(t); While (not termination condition) do { t= t+1; Select P(t) from P(t-1); Alter P(t); Evaluate P(t); } } Step 0: Initialization Step 1: Selection Step 2: Crossover Step 3: Mutation Step 4: Evaluation Step 5: Termination Test Step 0: Initialization Step 6: End

14. 3.GAs – Why use GAs to solve TSP ? (p.4) 2. Loop(Epoch): + Choosing 2 parents from from a random group of cities. + Crossover -> Offsprings (2) + Mutation + Evaluation + Use fitness function to remove some weaker individuals to keep the population size constant.

16. Fun fact about Salesmen today Today, young men think different !!!

17. Thank you for your attention !!!