1. Advanced AI – Session 6 Genetic Algorithm By: H.Nematzadeh

2. Objectives To understand the processes involved ie. GAs Basic flows –operator and parameters (roles, effects etc) To be able to apply GAs in solving optimisation problems

3. Evolutionary computation we are products of evolution, and thus by modelling the process of evolution, we might expect to create intelligent behaviour. Evolutionary computation simulates evolution on a computer. The result of such a simulation is a series of optimisation algorithms, usually based on a simple set of rules. Optimisation iteratively improves the quality of solutions until an optimal, or at least feasible, solution is found.

4. Nature like evolution is slow Evolution is a tortuously slow process from the human perspective, but the simulation of evolution on a computer does not take billions of years!

5. Natural evolution Evolution can be seen as a process leading to the maintenance of a population’s ability to survive and reproduce in a specific environment. This ability is called evolutionary fitness. Evolutionary fitness can also be viewed as a measure of organism’s ability to anticipate changes in its environment. The better an organism's fitness to the environment, the better its chances to survive

6. Rabbits & Foxes

7. Encoding Vs Evaluation

8. Class of searches techniques

9. Evolutionary Process

10. Mice & Cats: an evolutionary problem

11. The mice & cat algorithm

12. General evolution process

13. GA Vs Real life

14. Basic GA

16. Another way of looking at this…

17. Flowchart of GA

18. Another way of looking at this…

19. GA Process

20. Example 1 (not included in the book) burger and profit problem

21. Analysis

22. Fitness Evaluation

23. Selection

24. Crossover

25. Mutation

26. After 1 st run

27. Example 2: optimization of a one variable function

28. Steps in GA development

29. The entire universe of discourse

30. Operator parameters

31. Fitness function

32. The fitness functions and chromosomes location

33. Selection using roulette wheel One of the most commonly used chromosome selection techniques is the roulette wheel selection (Goldberg, 1989; Davis, 1991). Figure 7.4 illustrates the roulette wheel for our example. As you can see, each chromosome is given a slice of a circular roulette wheel.

34. Selection using roulette wheel

35. Crossover function

36. Mutation function

37. GA cycle

38. Example 3- 2 variables function