Download 7주 강의

7주 강의
Machine Evolution
Neural Networks
• Some aspects of learning in
biological systems
• 차의영 교수님 강의에서 배울 것
Evolutions
• Generations of descendants
– Production of descendants changed
from their parents
– Selective survival
• Search processes
• Searching for high peaks in the
hyperspace
Applications
• Function optimization
– The maximum of a function :::
John Holland
• Solving specific problems
• Control reactive agents
• Classifier systems
• Genetic programming
A program expressed as a tree
+
3
/
x
5
7
4
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
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 중
지
A robot in a Grid World
nw
n
ne
w
te
xt
e
sw
s
se
A wall following program
IF
AND
east
IF
OR
NOT
AND
n
ne
south
e
IF
OR
NOT
AND
e
se
west
s
OR
s
NOT
sw
north
w
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 벽
에 있는 방을 방문한 횟수로 평가  32 cells
이면 perfects; 10곳에서 출발하여 fitness 측
정
Generation of populations I
• (i+1)th generation
– 10%는 i-the generation에서 copy 
5000 populations에서 무작위로 7개를 선
택하여 가장 우수한 것을 선택
(tournament selection)
– 90%는 앞의 방법으로 두 프로그램(a
mother, a father)을 선택하여, 무작위로 선
정한 father의 subtree를 mother의
subtree에 넣는다 (crossover)
Crossover
Randomly chosen
crossover points
NOT
IF
AND
n
se
NOT
AND
se
IF
s
NOT
OR
NOT
e
se
nw
nouth
OR
Father program
west
w
Mother program
south
AND
se
NOT
IF
s
west
Child program
AND
se
NOT
nouth
Generation of populations II
– Mutation : 1%를 tournament로 선정 
무작위로 선택한 subtree를 제거하고, 1세
대에서 개체를 생성하는 방법으로 만들어
서 끼워넣는다
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)
The most fit individual in
generation 0
The most fit individuals in
generation 2
The most fit individuals in
generation 6
The most fit individuals in
generation 10
Fitness as a function of
generation number
350
300
Fitiness
250
200
150
100
50
0
0
1
2
3
4
5
6
7
Generation number
8
9
10
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?