1. 7주 강의
Machine Evolution

2. Neural Networks Some aspects of learning in biological systems
차의영 교수님 강의에서 배울 것

3. Evolutions Generations of descendants Search processes
Production of descendants changed from their parents
Selective survival
Search processes
Searching for high peaks in the hyperspace

4. Applications Function optimization Solving specific problems
The maximum of a function :::
John Holland
Solving specific problems
Control reactive agents
Classifier systems
Genetic programming

5. A program expressed as a tree

6. A robot to follow the wall around forever
Primitive functions : AND, OR, NOT, IF
Boolean functions
AND(x,y) = 0 if x = 0; else y
OR(x,y) = 1 if x = 1; else y
NOT(x) = 0 if x = 1; else 1
IF(x,y,Z) = y if x = 1; else z
Actions
North, east, south, west

7. A robot to follow the wall around forever
All of the action functions have their indicated effects unless the robot attempts to move into the wall
Sensory inputs ::: n, ne, e, se, s , sw, w, nw
만약 함수의 수행결과가 값이 없으면 중지

8. A robot in a Grid World

9. A wall following program

10. The GP process
Generation 0 (0세대): start with a population of random programs with functions, constants, and sensory inputs
5000 random programs
Final : Generation 62  60 steps 동안 벽에 있는 방을 방문한 횟수로 평가  32 cells이면 perfects; 10곳에서 출발하여 fitness 측정

11. Generation of populations I
(i+1)th generation
10%는 i-the generation에서 copy  5000 populations에서 무작위로 7개를 선택하여 가장 우수한 것을 선택 (tournament selection)
90%는 앞의 방법으로 두 프로그램(a mother, a father)을 선택하여, 무작위로 선정한 father의 subtree를 mother의 subtree에 넣는다 (crossover)

12. Crossover

13. Generation of populations II
Mutation : 1%를 tournament로 선정  무작위로 선택한 subtree를 제거하고, 1세대에서 개체를 생성하는 방법으로 만들어서 끼워넣는다

14. Evolving a wall-following robot
개별 프로그램의 예
(AND (sw) (ne)) (with fitness 0)
(OR (e) (west) (with fitness 5(?))
the best one ::: fitness = 92 (어떤 때)

15. The most fit individual in generation 0

16. The most fit individuals in generation 2

17. The most fit individuals in generation 6

18. The most fit individuals in generation 10

19. Fitness as a function of generation number

20. 숙제 Specify fitness functions for use in evolving agents that
Control an elevator
Control stop lights on a city main street
Determine what the words genotype and phenotype in evolutionary theory?
Why do you think mutation might or might no be helpful in evolutionary processes that use crossover?